A spatial intelligent hybrid reflection generation system and method that fuses explicit three-dimensional geometry projection with implicit diffusion generation

Abstract

This invention relates to the field of spatial intelligence technology, providing a spatial intelligent hybrid reflection generation system that integrates explicit 3D geometric projection and implicit diffusion generation. The system includes: a spatial reflection data acquisition and preprocessing module; an explicit 3D geometric modeling and mirror parameter analysis module; a virtual viewpoint construction and geometric coarse rendering module; a depth-condition-based bi-branch diffusion generation module; a reflection result geometric consistency constraint module; a phased training and model stabilization module; and an inference deployment and cross-scene adaptation module. Each module achieves hierarchical transmission and bi-directional feedback through spatial geometric information flow and generation constraint information flow, thereby constituting a spatial intelligent hybrid reflection generation system that combines generation capabilities with a spatial consistency guarantee mechanism. This invention effectively overcomes the shortcomings of existing generative reflection methods at the spatial intelligence level. This invention also provides a spatial intelligent hybrid reflection generation method that integrates explicit 3D geometric projection and implicit diffusion generation.

Description

Technical Field

[0001] This invention relates to the field of spatial intelligence technology, specifically to a spatial intelligence hybrid reflection generation system and method that integrates explicit 3D geometric projection and implicit diffusion generation, belonging to the interdisciplinary technology direction of computer vision, 3D spatial modeling, generative artificial intelligence and spatial computing. Background Technology

[0002] Current research has made some progress in deep learning-based image generation, spatial awareness modeling, and specular reflection synthesis. In particular, with the widespread application of diffusion generation models in image completion and redrawing tasks, related technologies have significantly improved visual naturalness and the representation of local details. However, existing technologies still have significant shortcomings in specular reflection generation, spatial geometric consistency preservation, and stable modeling of multi-object structures in complex spatial scenes, making it difficult to support spatial intelligent application systems with high requirements for spatial structure.

[0003] The specular reflection generation process lacks explicit geometric constraints: most existing reflection generation methods treat the mirrored area as a regular missing area in the image, redrawing the reflection content pixel-by-pixel through an end-to-end generation model. These methods typically rely on large-scale training data for statistical learning of the reflection's appearance, with the spatial structure, scale variations, and perspective relationships of objects in the reflection implicit in the model parameters, lacking explicit geometric modeling and projection constraints. When the reflecting object has a complex structure or asymmetrical shape, the generated results are prone to structural inconsistencies and proportional distortions.

[0004] Insufficient modeling of spatial perspective relationships: Mirror reflection essentially involves the geometric mapping relationship between the real camera viewpoint and the virtual viewpoint. However, existing diffusion generation or redrawing methods mostly only impose conditional constraints in the two-dimensional image space and fail to explicitly introduce spatial projection information from the virtual camera's perspective. This results in inconsistencies in perspective scaling, spatial orientation, and occlusion relationships in the generated reflection content, affecting the credibility of the generated results in real space.

[0005] The semantic consistency of reflections in complex multi-object scenes is difficult to guarantee: In scenes containing multiple objects, existing technologies often struggle to simultaneously maintain the relative spatial positions and semantic integrity of each object in the reflection. Due to the lack of a unified spatial geometric prior, the reflection content of different objects is prone to misalignment, merging, or local loss, especially when there are occlusion relationships or significant scale differences, further reducing the stability of the generative model.

[0006] Generative models have limited generalization ability for complex object structures: existing methods generally rely on a large amount of synthetic or real data for training, "memorizing" the appearance of reflections through a data-driven approach. When the training data does not sufficiently cover complex mechanical structures or special geometric shapes, the model often can only guess at invisible structures in practical applications, resulting in reflection results that do not match the real physical structure, thus limiting its application in high-precision spatial perception tasks.

[0007] Lack of explicit intermediate modes to connect geometric modeling and generation processes: Current reflection generation frameworks mostly map directly from visible images or depth conditions to the final reflection result, lacking intermediate expressions that can carry spatial geometry and perspective information. This results in low coupling between the geometric modeling module and the generation model, making it difficult to effectively constrain and interpret the generation process.

[0008] Current technologies lack a spatially intelligent method that can explicitly introduce 3D geometric projection and virtual viewpoint constraints during specular reflection generation, and particularly lack a technical solution for strongly guiding generative models through intermediate geometric modalities. In complex spatial scenes with multiple objects and high structural complexity, existing solutions struggle to simultaneously meet the comprehensive requirements of spatial geometric consistency, generation stability, and application reliability. Therefore, there is an urgent need to propose a spatially intelligent hybrid reflection generation technology that integrates explicit 3D geometric projection and implicit diffusion generation to effectively constrain spatial structure and perspective relationships in specular reflection, thereby improving the consistency and credibility of the generated results in real-world spatial applications. Summary of the Invention

[0009] One of the objectives of this invention is to provide a spatial intelligent hybrid reflection generation system that integrates explicit 3D geometric projection and implicit diffusion generation, in order to solve the problems of excessive reliance on data-driven generation, lack of geometric prior constraints, and inconsistent perspective relationships in complex object and multi-object scenes in existing specular reflection generation technologies.

[0010] This invention introduces a three-dimensional geometric projection mechanism based on a virtual camera perspective to construct a virtual viewpoint rendering map as an intermediate spatial modality, thereby achieving the synergistic fusion and mutual constraint of explicit geometric structures and implicit generative models. This unifies the spatial structure, perspective relationships, and semantic consistency of the reflected content during the generation process, significantly improving the stability, credibility, and engineering application reliability of the mirror reflection generation results in complex spatial scenes.

[0011] The second objective of this invention is to provide a spatial intelligent hybrid reflection generation method that integrates explicit three-dimensional geometric projection and implicit diffusion generation.

[0012] To achieve the above objectives, the present invention provides the following technical solution: A spatial intelligent hybrid reflection generation system integrating explicit 3D geometric projection and implicit diffusion generation includes: Spatial Reflection Data Acquisition and Preprocessing Module: Used to provide multi-source spatial reflection sample input data for the system training and inference phases; Explicit 3D Geometric Modeling and Mirror Parameter Resolution Module: Used to recover or resolve the 3D geometric structure of the scene from the input data, and to explicitly model the geometric properties of the mirror in space; Virtual viewpoint construction and geometric coarse rendering module: used to construct virtual camera viewpoints under explicit 3D geometric constraints and generate corresponding virtual viewpoint geometric rendering results; A depth-based bi-branch diffusion generation module is used to generate specular reflection content under geometric prior constraints. Geometric Consistency Constraint Module for Reflection Results: Used to perform geometric consistency verification and structural constraints on the specular reflection results obtained by the dual-branch diffusion generation module; The phased training and model stabilization module is used to systematically train and stabilize the bibranch diffusion generation model, ensuring that the bibranch diffusion generation model maintains its effective response to geometric priors under different complexity scenarios. Inference Deployment and Cross-Scene Adaptation Module: Used to deploy the trained spatial intelligent hybrid reflection generation system to the actual application environment and achieve the ability to adapt to different scene conditions; Each module achieves hierarchical transmission and bidirectional feedback through spatial geometric information flow and generation constraint information flow, thereby forming a spatial intelligent hybrid reflection generation system that combines generation capability and spatial consistency guarantee mechanism.

[0013] Preferably, the spatial reflection data acquisition and preprocessing module includes: Spatial Data Unified Calibration Unit: Used to perform unified coordinate system calibration on the image, depth information, mirror area and camera parameters in the input data to ensure that subsequent geometric modeling and virtual viewpoint calculation have a consistent reference framework; Reflection region resolution unit: used to automatically or semi-automatically identify mirror regions and generate corresponding reflection masks, providing basic input for virtual viewpoint construction and constraint generation; Geometric Auxiliary Information Processing Unit: Used to extract or estimate the spatial scale, relative positional relationships, and occlusion information of the scene from the input data, in order to support subsequent explicit 3D geometric modeling.

[0014] Preferably, the explicit 3D geometric modeling and mirror parameter analysis module includes: 3D spatial structure modeling unit: used to reconstruct the spatial position, scale and orientation of objects in a scene based on depth maps, point clouds or external 3D information, forming a 3D geometric representation that can be used for projection calculation; Mirror plane parameter analysis unit: used to perform parametric modeling of the mirror area, analyze the normal vector, position and boundary range of the mirror plane for subsequent virtual camera viewpoint calculation; Real camera parameter acquisition unit: used to acquire or estimate the intrinsic and extrinsic parameters of the real camera, providing a basis for constructing the virtual camera viewpoint.

[0015] Preferably, the virtual viewpoint construction and geometry coarse rendering module includes: Virtual camera viewpoint calculation unit: used to calculate the mirror symmetry relationship based on the mirror plane parameters and the real camera pose, and to construct a virtual camera viewpoint and its projection matrix that conform to the laws of physical reflection; Virtual viewpoint geometry rendering unit: used for fast geometry rendering of the scene from a virtual viewpoint, generating a 2D virtual viewpoint rendering map that emphasizes object outlines, occlusion relationships and perspective scaling information; Geometric prior encoding unit: used to encode the virtual viewpoint rendering map into a geometric prior feature form that can be used by the diffusion generation model, for structural constraints in the subsequent diffusion generation process.

[0016] Preferably, the depth-based dual-branch diffusion generation module adopts a dual-branch diffusion generation architecture, including: Visible view texture and semantic branch: used to extract local texture features and semantic representations from input images and depth information, providing a visual detail basis for reflection generation; Virtual viewpoint geometry prior branch: used to encode the global geometry and perspective relationships contained in the virtual viewpoint rendering graph; During the diffusion denoising process, the system continuously injects geometric priors through a cross-branch fusion mechanism, so that the generation process always follows the predetermined spatial structure while gradually refining the texture.

[0017] Preferably, the geometric consistency constraint module for reflection results includes: Geometric consistency detection unit: Based on the correspondence between the virtual viewpoint rendering map and the final generated reflection image, it performs consistency detection on key geometric elements; Structural conflict identification unit: When a significant structural conflict is detected in the generated reflection content, the system marks the conflict area and generates a corresponding constraint feedback signal; Consistency Feedback Modulation Unit: Used to feed back the geometric consistency detection results to the depth-condition-based bi-branch diffusion generation module. During the generation process, local redrawing or condition weight adjustment is performed so that the diffusion generation model more strongly follows the geometric prior in subsequent iterations, thereby gradually converging to a structurally stable reflection result.

[0018] Preferably, the phased training and model stabilization module includes: Training phase division unit: used to divide the training process of the diffusion generation model into several phases according to the complexity of the input scene; each phase adopts different geometric prior weights and generative degrees of freedom configurations, so that the diffusion generation model gradually learns to balance geometric constraints and generative capabilities under complex spatial conditions; Geometric constraint weight scheduling unit: used to dynamically adjust the influence weight of virtual viewpoint geometric prior in the diffusion generation model during training, so that the diffusion generation model relies more on geometric structure in the early stage and gradually improves texture and detail generation ability in the later stage, thereby avoiding overfitting to geometric contours or texture details. Model stability monitoring unit: Used to continuously monitor the generation stability indicators of the diffusion generation model in the validation set or inference stage. When the performance of the diffusion generation model shows a deterioration trend, it automatically triggers the training parameter adjustment or rollback mechanism.

[0019] Preferably, the inference deployment and cross-scenario adaptation module includes: Inference process control unit: During the actual inference stage, the system automatically determines whether to enable the virtual viewpoint rendering module and its accuracy level based on the input scene conditions, thereby dynamically balancing real-time performance and generation quality. Scene Adaptive Adjustment Unit: Used to dynamically adjust the geometric prior injection strength and diffusion generation steps for different application scenarios, so that the system can run stably under different hardware and environmental conditions; Cross-scene generalization support unit: It is used to gradually optimize the system parameter configuration through long-term statistical analysis of the consistency index of the generated results, so that the model can maintain reasonable generation behavior under unseen spatial layout and reflection conditions.

[0020] A spatial intelligent hybrid reflection generation method that integrates explicit 3D geometric projection and implicit diffusion generation includes: By acquiring visible view images, depth information, mirror area masks, and camera parameters, the basic configuration for the reflection generation task is completed, and data consistency correction is used to ensure the uniformity of input data in space and scale. Explicit 3D geometric modeling is performed on the input scene: by performing structural analysis on the depth information, a 3D geometric representation of the scene is constructed, and planar parameter analysis is performed on the mirror area to clarify the position, orientation and effective range of the mirror in space; Based on the geometric parameters of the mirror and the spatial relationship of the camera, a virtual viewpoint is constructed: the position and orientation of the virtual viewpoint are determined by geometric symmetry transformation, and the corresponding spatial projection relationship is established to ensure that the virtual viewpoint conforms to the real physical reflection law; Perform geometric coarse rendering on the 3D scene structure under the virtual viewpoint to generate a virtual viewpoint rendering result with correct structure but simplified texture, and encapsulate it into a unified geometric intermediate modality to be used as a strong geometric prior for subsequent generation processes. Mirror reflection generation is performed under the geometric intermediate mode constraint: the mirror reflection generation process is carried out in stages. First, the overall spatial structure is determined, and then the details are gradually added. It only applies to the mirror area to ensure that the non-reflective area retains the original content. Perform geometric consistency verification on the generated results: detect the consistency of perspective, scale and occlusion by comparing the structural relationship between the generated reflection results and the virtual viewpoint geometric rendering; Once the generated result passes the consistency verification, the final specular reflection generation result is output and integrated into the downstream application system. At the same time, key indicators during the operation are recorded for subsequent adaptive optimization.

[0021] Preferably, a virtual viewpoint constrained diffusion generation algorithm is used to perform geometric coarse rendering of the 3D scene structure under a virtual viewpoint; The virtual viewpoint constraint diffusion generation algorithm includes the following steps: The visible view image, depth information, specular mask, and camera parameters are obtained as input. Based on the mirror plane parameters and the actual camera pose, the virtual camera viewpoint is calculated, and the corresponding virtual viewpoint geometric rendering is generated. The virtual viewpoint rendering map is encoded as geometric prior features and used as a conditional input to the geometric branch of the diffusion generation model; In the diffusion denoising process, geometric prior features are fused with visible view features at each time step to guide the gradual generation of the reflection region.

[0022] Compared with existing specular reflection generation methods that mainly rely on end-to-end generative deep learning or pure image redrawing strategies, this invention has significant advantages in terms of spatial geometric consistency, perspective stability, reliability of complex structure reflection generation, and engineering controllability, including: 1. The specular reflection generation process has stronger spatial interpretability. This invention explicitly introduces virtual viewpoint rendering as an intermediate geometric modality into the generation process, thus explicitly expressing the spatial perspective relationships implicit in the network weights of traditional generative models. The reflected content is no longer solely determined by statistical learning results, but is constrained by computable and analyzable geometric projection results, giving the reflection generation process a clear physical meaning at the spatial structure level. Compared to existing black-box reflection redrawing methods, this invention can clearly explain the perspective source and spatial composition of the reflected content, significantly improving the system's understandability and debuggability in engineering applications.

[0023] 2. The geometric and perspective consistency of reflected content is significantly enhanced. By introducing explicit 3D geometric modeling and a virtual camera viewpoint projection mechanism, this invention imposes strong constraints on object scale changes, perspective scaling relationships, and occlusion order during reflection generation. The generated model is consistently guided by geometric priors during the denoising and redrawing stages, effectively avoiding common problems in existing methods such as perspective distortion, object scale distortion, and spatial structure inconsistency, making the generated reflection results more geometrically consistent with real imaging laws.

[0024] 3. Significantly improved generation stability for complex structures and multi-object scenes. For complex mechanical structures, asymmetrical objects, and multi-object interaction scenarios, this invention predetermines the spatial layout of invisible areas in reflections through virtual viewpoint rendering, fundamentally reducing the space for free guessing of "unknown structures" by the generative model. Compared to existing methods that rely on large-scale data to memorize appearance patterns, this invention is less prone to structural confusion or semantic mismatches in complex scenes, significantly improving the stability and reliability of reflection generation results in highly complex scenarios.

[0025] 4. The engineering controllability and robustness of the generated results are significantly improved. This invention introduces a geometric consistency detection and feedback mechanism in the post-generation stage to perform structural-level verification and necessary corrections on the reflection results, forming a closed-loop process of "generation-verification-optimization". When local structural conflicts or geometric deviations occur in the generated results, the system can automatically trigger constraint modulation or local regeneration, thereby suppressing error accumulation. This design enables the system to maintain relatively stable generation performance under different scene conditions and different input qualities, enhancing overall robustness.

[0026] 5. Stronger adaptability to real-world scenarios and cross-application conditions Because the core constraints of this invention originate from physical geometric projection relationships rather than specific data distributions, the system can still maintain reasonable reflection generation behavior when faced with different spatial layouts, different material reflection characteristics, or different acquisition device conditions. This characteristic enables the invention to have strong cross-scenario generalization capabilities in various spatial intelligence applications such as indoor space reconstruction, augmented reality content generation, robot visual perception, and digital twins.

[0027] This invention achieves the unification of spatial structure, perspective relationship and generation flexibility in specular reflection generation through an innovative technical architecture of "explicit three-dimensional geometric projection + virtual viewpoint intermediate mode + implicit diffusion generation constraint". It significantly improves the consistency, stability and engineering application value of reflection generation results in complex spatial environments, and can effectively make up for the shortcomings of existing generative reflection methods in spatial intelligence. It has broad application prospects and promotion value.

[0028] The summary section is provided to present the chosen concepts in a simplified form, which will be further described in the detailed description below. The summary section is not intended to identify essential or necessary features of this disclosure, nor is it intended to limit the scope of this disclosure. Attached Figure Description

[0029] The above and other objects, features and advantages of this disclosure will become more apparent from the accompanying drawings, in which like reference numerals generally denote like parts.

[0030] Figure 1 This is a diagram illustrating the overall architecture of a spatial intelligent hybrid reflection generation system in one embodiment of the present invention. Figure 2 This is a flowchart of a dual-branch diffusion generation module in one embodiment of the present invention; Figure 3 This is a flowchart of the overall operation of the intermediate intelligent hybrid reflection generation system in one embodiment of the present invention (executed in stages). Figure 4 This is a flowchart of virtual viewpoint construction and geometric coarse rendering in one embodiment of the present invention; Figure 5 This is a flowchart illustrating the geometric consistency constraints and feedback closed-loop of reflection results in one embodiment of the present invention. Detailed Implementation

[0031] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0032] The term "comprising" and its variations as used herein signify open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0033] This invention focuses on introducing physically consistent spatial priors in complex real-world scenarios using 3D geometric projection and virtual viewpoint modeling, combined with a depth-based diffusion generation model, to achieve highly consistent generation and reconstruction of unobservable spatial content in mirror reflection areas. It can be widely applied in indoor spatial perception and reconstruction, robot vision and environmental understanding, augmented reality and virtual reality content generation, digital twins and intelligent interaction systems, intelligent security and inspection analysis, and other spatial intelligent application scenarios with high requirements for spatial geometric consistency and visual realism.

[0034] This invention provides a spatial intelligent hybrid reflection generation system and method that integrates explicit 3D geometric projection and implicit diffusion generation. It aims to introduce virtual viewpoint rendering as an intermediate spatial modality to explicitly model the spatial perspective relationship implicit in the specular reflection generation process and form a collaborative constraint with the generative diffusion model. This solves the problems of inconsistent perspective relationships, geometric distortion, and semantic distortion that are common in existing reflection generation methods in complex object structures, multi-object scenes, and real-world application environments.

[0035] Unlike existing specular reflection redrawing methods based on large-scale data fitting, this invention does not rely on "memorizing" the appearance of reflections by expanding the data scale. Instead, it starts from the spatial imaging mechanism and models the specular reflection problem as a geometric mapping problem between the real camera viewpoint and the virtual camera viewpoint. By introducing a virtual viewpoint rendering result that conforms to the physical laws of reflection before generating the model, the diffusion generation process is subject to explicit three-dimensional geometric constraints at the structural level, thereby effectively reducing the space for free guessing in unobservable areas of the generation model.

[0036] This invention constructs a spatial intelligent reflection generation framework at the system level, which consists of "geometric modeling - virtual viewpoint projection - generation constraints - result consistency verification". This framework achieves a balance between visual naturalness and spatial consistency in the generated results, significantly improving the stability, controllability and reliability of reflection content in complex real-world scenarios.

[0037] The following detailed description, with reference to a specific embodiment of the present invention, illustrates a spatial intelligent hybrid reflection generation system and method that integrates explicit three-dimensional geometric projection and implicit diffusion generation. It should be understood that this embodiment is merely for illustrating the technical solution of the present invention and is not intended to limit the scope of protection of the present invention. Equivalent substitutions or modifications made to this embodiment by those skilled in the art without creative effort should all fall within the scope of protection of the present invention. Example 1:

[0038] The spatial intelligent hybrid reflection generation system of this invention, which integrates explicit 3D geometric projection and implicit diffusion generation, adopts a multi-layered collaborative architecture of "spatial perception and input parsing - explicit 3D geometric modeling - virtual viewpoint rendering - dual-branch diffusion generation - consistency constraints and feedback optimization". For example... Figure 1 As shown, the system includes at least the following seven main functional modules: Spatial reflection data acquisition and preprocessing module; Explicit 3D geometric modeling and mirror parameter analysis module; Virtual viewpoint construction and geometry coarse rendering module; A depth-condition-based dual-branch diffusion generation module; Geometric consistency constraint module for reflection results; Phased training and model stabilization module; Inference deployment and cross-scenario adaptation module; Each module achieves hierarchical transmission and bidirectional feedback through spatial geometric information flow and generation constraint information flow, thereby forming a spatial intelligent hybrid reflection generation system that combines generation capability and spatial consistency guarantee mechanism.

[0039] The following explains each technical module and its implementation principle: 1. Spatial Reflection Data Acquisition and Preprocessing Module The spatial reflection data acquisition and preprocessing module is used to provide multi-source spatial reflection sample input for the system training and inference stages. Its function is to uniformly organize, standardize and calibrate the geometric consistency of existing or externally acquired spatial reflection data, rather than to propose a new dataset construction scheme.

[0040] The spatial reflection data acquisition and preprocessing module can access reflection sample data from various sources, including but not limited to: synthetic reflection data generated by 3D scene rendering tools, existing public reflection datasets, and reflection image data collected in real scenes by cameras and depth sensors.

[0041] The spatial reflection data acquisition and preprocessing module mainly includes the following sub-units: (1) Spatial data unified calibration unit: used to perform unified coordinate system calibration on the image, depth information, mirror area and camera parameters in the input data to ensure that subsequent geometric modeling and virtual viewpoint calculation have a consistent reference framework.

[0042] (2) Reflection area resolution unit: automatically or semi-automatically identify the mirror area and generate the corresponding reflection mask, providing basic input for virtual viewpoint construction and generation constraints.

[0043] (3) Geometric Auxiliary Information Processing Unit: Extracts or estimates the spatial scale, relative positional relationship and occlusion information of the scene from the input data to support subsequent explicit 3D geometric modeling.

[0044] The output of the spatial reflection data acquisition and preprocessing module is a set of multimodal reflection samples that have undergone spatial consistency processing, which are used only as input data for the system.

[0045] 2. Explicit 3D Geometric Modeling and Mirror Parameter Analysis Module This module is used to recover or parse the three-dimensional geometry of the scene from the input data and to explicitly model the geometric properties of the mirror in space. It is a key foundational module for introducing geometric priors in this invention.

[0046] The explicit 3D geometric modeling and mirror parameter analysis module includes: (1) Three-dimensional spatial structure modeling unit: used to reconstruct the spatial position, scale and orientation of objects in the scene based on depth map, point cloud or external three-dimensional information, and form a three-dimensional geometric representation that can be used for projection calculation.

[0047] (2) Mirror plane parameter analysis unit: used to perform parametric modeling of the mirror area, analyze the normal vector, position and boundary range of the mirror plane, and use it for subsequent virtual camera viewpoint calculation.

[0048] (3) Real camera parameter acquisition unit: used to acquire or estimate the intrinsic and extrinsic parameters of the real camera, providing a basis for constructing the virtual camera view.

[0049] 3. Virtual viewpoint construction and geometry coarse rendering module This module is used to construct a virtual camera viewpoint under explicit 3D geometric constraints and generate the corresponding virtual viewpoint geometric rendering results. It is one of the core innovations of this invention that distinguishes it from existing reflection generation technologies.

[0050] The virtual viewpoint construction and geometry coarse rendering module mainly includes: (1) Virtual camera viewpoint calculation unit: used to calculate the mirror symmetry relationship based on the mirror plane parameters and the real camera pose, and to construct the virtual camera viewpoint and its projection matrix that conform to the physical reflection law.

[0051] (2) Virtual Viewpoint Geometry Rendering Unit: Used to perform fast geometry rendering of the scene from a virtual viewpoint, generating a two-dimensional virtual viewpoint rendering map that emphasizes object outlines, occlusion relationships, and perspective scaling information. The rendering result is a coarse rendering, prioritizing geometric accuracy over texture realism.

[0052] (3) Geometric prior coding unit: used to encode the virtual viewpoint rendering map into a geometric prior feature form that can be used by the diffusion generation model, and used for structural constraints in the subsequent diffusion generation process.

[0053] 4. Depth-based dual-branch diffusion generation module This module is used to generate specular reflection content under geometric prior constraints and is the core of execution for realizing the fusion of explicit geometric projection and implicit generative model.

[0054] like Figure 2 As shown, the depth-based dual-branch diffusion generation module adopts a dual-branch diffusion generation architecture, including: (1) Visible view texture and semantic branch: used to extract local texture features and semantic representations from input images and depth information, providing a visual detail basis for reflection generation.

[0055] (2) Virtual viewpoint geometry prior branch: used to encode the global geometric structure and perspective relationships contained in the virtual viewpoint rendering graph.

[0056] During the diffusion denoising process, the system continuously injects geometric priors through a cross-branch fusion mechanism, so that the generation process always follows the predetermined spatial structure while gradually refining the texture.

[0057] 5. Reflection Result Geometric Consistency Constraint Module This module performs geometric consistency verification and structural constraints on the specular reflection results obtained by the bi-branch diffusion generation module, serving as an important supplementary module to ensure the usability and spatial reliability of the generation results. Unlike traditional methods that rely solely on implicit constraints of the generation model, this invention introduces an explicit geometric consistency detection and feedback mechanism in the post-generation stage to perform structural-level verification of the generation results.

[0058] The geometric consistency constraint module for reflection results mainly includes the following sub-units: (1) Geometric Consistency Detection Unit: Based on the correspondence between the virtual viewpoint rendering image and the final generated reflection image, it performs consistency detection on key geometric elements, including but not limited to: Alignment degree of object outlines; The relationship between spatial scale and perspective scaling; Consistency of the position of the main structural boundaries; Does the occlusion relationship match the virtual viewpoint projection result?

[0059] By calculating the geometric deviation index between the generated result and the geometric prior of the virtual viewpoint, it is determined whether the reflection result meets the preset spatial consistency constraint.

[0060] (2) Structural conflict identification unit: When obvious structural conflicts (such as object intersection, structural breakage or scale abnormality) are detected in the generated reflection content, the system marks the conflict area and generates the corresponding constraint feedback signal.

[0061] (3) Consistency Feedback Modulation Unit: Used to feed back the geometric consistency detection results to the diffusion generation module, and locally redraw or adjust the condition weights of the generation process when necessary, so that the diffusion generation model follows the geometric prior more strongly in subsequent iterations, thereby gradually converging to the structurally stable reflection result.

[0062] By using the geometric consistency constraint module for reflection results, this invention establishes a second geometric defense at the level of generated results, further reducing the probability of the diffusion generation model producing uncontrollable results in complex structural scenarios.

[0063] 6. Phased Training and Model Stabilization Module This module is used for systematic training and stabilization control of the diffusion generative model, ensuring that the model maintains effective response to geometric priors under scenarios of varying complexity. This module does not rely on a specific dataset itself, but rather focuses on phased control during the training process and behavioral constraints on the diffusion generative model.

[0064] The phased training and model stabilization module mainly implements the following functions: (1) Training phase division unit: used to divide the training process of the diffusion generation model into several phases according to the complexity of the input scene, such as: single or low-complexity object reflection phase; multi-object and occlusion relationship enhancement phase; real scene or high noise condition adaptation phase.

[0065] Different geometric prior weights and generative degrees of freedom are configured for each stage, enabling the diffusion generative model to gradually learn to balance geometric constraints and generative capabilities under complex spatial conditions.

[0066] (2) Geometric constraint weight scheduling unit: used to dynamically adjust the influence weight of virtual viewpoint geometric prior in the diffusion generation model during the training process, so that the diffusion generation model relies more on geometric structure in the early stage and gradually improves the ability to generate texture and details in the later stage, thereby avoiding overfitting to geometric contours or texture details.

[0067] (3) Model stability monitoring unit: used to continuously monitor the generation stability indicators of the diffusion generation model in the validation set or inference stage, including structural consistency, perspective error and local distortion rate. When the diffusion generation model shows a degenerate trend, the training parameter adjustment or rollback mechanism is automatically triggered.

[0068] Through phased training and model stabilization modules, this invention ensures that the diffusion generative model maintains good generalization ability and training stability even with the introduction of strong geometric priors.

[0069] 7. Inference Deployment and Cross-Scenario Adaptation Module This module is used to deploy the trained hybrid reflection generation system to a real-world application environment and enable it to adapt to different scenario conditions.

[0070] The inference deployment and cross-scenario adaptation module mainly includes: (1) Reasoning process control unit: During the actual reasoning stage, the system automatically determines whether to enable the virtual viewpoint rendering module and its accuracy level based on the input scene conditions, thereby making a dynamic trade-off between real-time performance and generation quality.

[0071] (2) Scene adaptive adjustment unit: used to dynamically adjust the geometric prior injection intensity and diffusion generation steps for different application scenarios (such as indoor, outdoor, low light or high reflection environment) so that the system can run stably under different hardware and environmental conditions.

[0072] (3) Cross-scene generalization support unit: It is used to gradually optimize the system parameter configuration through long-term statistical analysis of the consistency index of the generated results, so that the model can maintain reasonable generation behavior under unseen spatial layout and reflection conditions.

[0073] The core reflection generation and constraint algorithm of this invention includes: (1) Virtual viewpoint constraint diffusion generation algorithm This algorithm is one of the core computational logics of this invention, used to continuously introduce virtual viewpoint geometric priors during the diffusion generation process.

[0074] The algorithm mainly includes the following steps: Step C1: Input Parsing The system takes the visible view image, depth information, specular mask, and camera parameters as input.

[0075] Step C2: Virtual viewpoint calculation and rendering Based on the mirror plane parameters and the actual camera pose, the virtual camera viewpoint is calculated, and the corresponding virtual viewpoint geometric rendering is generated.

[0076] Step C3: Geometric Prior Encoding The virtual viewpoint rendering map is encoded as geometric prior features and used as conditional input to the geometric branch of the diffusion generative model.

[0077] Step C4: Diffusion Denoising and Conditional Fusion During the diffusion denoising process, the system fuses geometric prior features with visible viewpoint features at each time step to guide the gradual generation of the reflection region.

[0078] Step C5: Consistency Detection and Feedback After generation, a geometric consistency check is performed on the reflection results, triggering local regeneration or parameter adjustment if necessary.

[0079] (2) Reflection Geometric Consistency Constraint Algorithm This algorithm is used to maintain the spatial plausibility of reflection results during the generation and post-processing stages.

[0080] The reflection geometric consistency constraint algorithm defines a set of reflection consistency criteria, including: Geometric profile consistency criterion; Criterion for consistency of perspective proportions; Criteria for determining the reasonableness of occlusion relationships.

[0081] After each generation, the system calculates the above criteria and decides whether to perform feedback correction based on the magnitude of the deviation, thus forming a closed loop of generation-verification-correction.

[0082] This invention discloses the overall operation flow of a spatial intelligent hybrid reflection generation system that integrates explicit three-dimensional geometric projection and implicit diffusion generation, as follows: Figure 3 As shown, it includes the following stages: Phase D1: Input Acquisition and Spatial Resolution.

[0083] Acquire scene images, depth and mirror information, and complete preliminary geometric modeling.

[0084] Phase D2: Virtual viewpoint construction and rendering.

[0085] Construct a virtual camera viewpoint and generate geometric rendering priors.

[0086] Stage D3: Diffusion generation and geometric constraint fusion.

[0087] Generate specular reflection content guided by geometric priors.

[0088] Phase D4: Consistency check and result optimization.

[0089] Perform geometric consistency checks on the generated results and make necessary corrections.

[0090] Phase D5: Results output and application deployment.

[0091] The final reflection generation result is output and used in downstream space intelligence applications. Example 2:

[0092] The following describes a spatial intelligent hybrid reflection generation method that integrates explicit three-dimensional geometric projection and implicit diffusion generation according to the present invention.

[0093] This embodiment uses a reflection generation scenario with mirrored or highly reflective surfaces in a complex indoor space as the application background to illustrate how the present invention completes the entire process from input acquisition, geometric modeling, virtual viewpoint construction, reflection generation to consistency verification in a real engineering system.

[0094] A spatial intelligent hybrid reflection generation method that integrates explicit 3D geometric projection and implicit diffusion generation includes: S1 input acquisition and reflection task initialization, including: S11 Input Data Acquisition The system obtains the basic input information used for reflection generation through a unified data interface, including: Raw, visible view images are used to provide texture and semantic information about the real scene; The spatial depth information corresponding to the image is used to describe the spatial distance relationship between each pixel in the scene; A mirror area mask is used to identify the image area where reflective content needs to be generated; Camera parameter information is used for subsequent spatial projection and viewpoint calculation.

[0095] The input data can come from depth cameras, multi-view reconstruction results, or other spatial sensing devices, as long as they can provide a reliable correspondence between images and depth.

[0096] S12 Data Consistency Correction and Preprocessing To ensure the stability of subsequent geometric calculations, the system first performs consistency preprocessing on the input data, including: Spatial filtering is performed on the depth information to remove isolated noise points and outlier measurements; Align the image and depth data at resolution to ensure a one-to-one correspondence at the pixel level; All input data are uniformly mapped to the same camera coordinate system to form a standardized spatial input state.

[0097] Through the above processing, the system obtains an initial input state that remains consistent in both time and space, providing a reliable foundation for subsequent explicit 3D geometric modeling.

[0098] S2 explicit 3D geometric modeling and mirror parameter analysis, including: S21 Scene 3D Structure Construction The system constructs a 3D geometric representation of the scene based on depth information. This 3D geometric representation does not require a high-precision complete reconstruction of the scene, but focuses on the geometric structure information required for reflection generation, including: the spatial position and approximate shape of the main objects; the hierarchical relationship between different objects; and the distribution of spatial regions that have a reflection relationship with the mirror surface.

[0099] In engineering implementation, the system uses a combination of sparse 3D point sets and planar constraints to construct the scene structure, so as to reduce computational overhead while ensuring geometric rationality.

[0100] S22 Mirror Plane Geometric Parameter Calculation Within the depth range corresponding to the mask of the mirror region, the system performs geometric analysis on the mirror and calculates the parameter information of the mirror in space, including: The normal direction of the mirror plane; The spatial position of the mirror in the camera coordinate system; The effective boundary range of the mirror surface.

[0101] The aforementioned mirror parameters are used to accurately describe the geometric properties of the mirror in three-dimensional space and are key input conditions for the subsequent construction of virtual viewpoints.

[0102] The relationship between the S23 camera and the mirror space has been confirmed. The system combines camera parameters and mirror geometry parameters to determine the spatial relationship between the camera viewpoint and the mirror, including the geometric relationship between the camera line of sight and the mirror normal, as well as the projection range of the mirror in the camera's field of view.

[0103] This step is used to ensure that the virtual viewpoints constructed subsequently meet the laws of real physical reflection, and to avoid generating reflection perspectives that do not conform to actual spatial relationships.

[0104] S3 virtual viewpoint construction and geometry coarse rendering generation, such as Figure 4 As shown, it includes: S31 Virtual Viewpoint Calculation Based on the principle of mirror reflection in geometric optics, the system uses the mirror plane as a symmetry reference to perform spatial symmetry transformation on the real camera viewpoint, and calculates the position and orientation of the virtual viewpoint.

[0105] This virtual viewpoint is physically equivalent to the ideal observation position for observing the content reflected in a mirror. Its spatial position and line of sight are strictly determined by the geometric relationship of the mirror, rather than inferred by the learning model.

[0106] S32 Virtual Viewpoint Projection Relationship Establishment After determining the virtual viewpoint, the system constructs the projection relationship corresponding to the virtual viewpoint based on its spatial location and camera parameters, which is used to map the three-dimensional scene structure onto the two-dimensional imaging plane under the virtual viewpoint.

[0107] This projection relationship fully follows the real imaging model, thus ensuring the physical consistency of the reflected content in terms of scale changes, perspective scaling, and occlusion relationships.

[0108] S33 Geometry Coarse Rendering Generation The system utilizes virtual viewpoint projection relationships to perform geometric rendering on the constructed 3D scene structure, generating 2D geometric rendering results from the virtual viewpoint.

[0109] The two-dimensional geometry rendering result has the following characteristics: Accurately reflects the spatial outline and relative position of an object from a reflection perspective; To accurately represent the perspective scaling relationship between objects at different distances; Clearly indicate the order of occlusion between objects.

[0110] The two-dimensional geometry rendering result does not pursue realistic textures or lighting effects; its main function is to provide a spatial prior with correct structure.

[0111] S34 Geometry Intermediate Modal Package The system encapsulates the virtual viewpoint geometric rendering results and their corresponding spatial information into a unified intermediate geometric representation for use in the subsequent reflection generation process.

[0112] This intermediate geometric representation serves as an intermediate mode connecting explicit geometric modeling and implicit generative models in the system, and is a key technical means for realizing geometric constraint generation in this invention.

[0113] The S4 reflection generation process based on geometric intermediate mode constraints includes: S41 Generation Conditions Construction and Input Organization After completing the construction of the virtual viewpoint geometric intermediate modes, the system enters the reflection generation stage. The system organizes the input conditions required for the generation task into two types of information channels: The first category is the visible view information channel, which includes the original visible view image and the corresponding depth information, used to provide texture details, material distribution and semantic clues in the real scene; The second category is the geometric intermediate modal channel, which includes the geometric rendering results under the virtual viewpoint and its corresponding spatial structure information, used to provide strong constraints on the spatial structure and perspective relationship of the reflected content.

[0114] During the input organization stage, the system performs spatial alignment processing on the two types of information to ensure that they are completely consistent within the range of resolution, coordinate mapping, and mirror area mask, thereby avoiding generation deviations caused by condition misalignment.

[0115] S42 geometric constraint-guided stepwise generation mechanism The system employs a progressively refined generation mechanism to generate content for the mirror region. Unlike the traditional method of predicting reflection results in one go, the generation process in this embodiment is designed as multiple consecutive generation stages, each constrained by a geometric intermediate mode.

[0116] In the initial generation stage, the system prioritizes determining the overall structural layout of the reflected content based on the intermediate geometric modes, including the basic shape, spatial position, and relative proportions of objects. During this stage, the generation process strictly adheres to the virtual viewpoint's geometric projection results, limiting the model's free inference range for invisible areas.

[0117] During the mid-generation stage, while maintaining the overall structure, the system introduces semantic and texture features from the visible view information to supplement the details of the reflected content, such as changes in surface material, edge transitions, and local texture consistency.

[0118] In the later stages of generation, the system performs overall smoothing and consistency optimization on the generated results to ensure that the transition between reflective and non-reflective areas at the boundary is natural and there are no obvious breaks or visual abrupt changes.

[0119] S43 The mirror area is locally generated while the non-mirror area is preserved. To avoid unnecessary impact on the original scene, the system only performs reflection generation operations on the areas identified by the mirror area mask during the generation process. Non-mirror areas directly retain the original image content and do not participate in the generation calculation.

[0120] This design not only reduces computational complexity, but also ensures that the generated results only affect the areas that actually need reflection completion, thus avoiding interference from the generated model with the overall image structure.

[0121] S44 Output Results After completing the above generation steps, the system outputs a preliminary specular reflection image. This image is consistent with the virtual viewpoint's geometric projection result in terms of overall spatial structure and has a complete representation of the reflection content.

[0122] Geometric consistency verification and feedback correction of S5 reflection generation results, such as Figure 5 As shown, it includes: S51 Geometric Consistency Inspection To further enhance the credibility of the generated reflections in real space, the system performs a geometric consistency check on the reflection results after generation. The check includes: the correspondence between the object outlines in the reflections and the geometric rendering results from the virtual viewpoint; and whether the scale changes of the objects in the reflections conform to the perspective rules under the virtual viewpoint. Does the occlusion order between different objects match the geometric projection result?

[0123] The system calculates the geometric consistency score of the generated results by comprehensively evaluating the above indicators.

[0124] S52 Abnormal Structure Identification and Region Localization When the geometric consistency score is detected to be lower than the preset threshold, the system further analyzes the location and type of the inconsistent areas, including but not limited to: obvious misalignment of object structure; abnormal scale ratio; conflict of occlusion relationship.

[0125] The system marks the aforementioned abnormal areas using spatial masks to provide precise location for subsequent corrections.

[0126] S53 Feedback Correction and Local Regeneration For the marked abnormal regions, the system triggers a feedback correction mechanism. This mechanism does not regenerate the entire reflection region, but only performs a local regeneration operation on the abnormal regions.

[0127] During the local regeneration process, the system enhances the weight of the geometric intermediate modes in the generation conditions, further compresses the degrees of freedom of the generated model, and makes the correction process more strictly follow the geometric projection constraints.

[0128] This local correction mechanism can effectively prevent the overall reflection results from being destroyed, while significantly improving the generation quality of structurally unstable regions.

[0129] S54 verification passed and results confirmed. Once the generated result meets the preset requirements in the geometric consistency check, the system marks the result as verified and uses it as the final reflection generation output.

[0130] S6 Output Integration and System Operation Closed Loop S61 Results Output and System Integration The final generated mirror reflection image can be directly output to downstream application systems, including but not limited to augmented reality display modules, spatial reconstruction systems, or robot vision perception modules.

[0131] The output maintains consistency with the original input image in resolution and coordinate system, making it easy to directly replace or overlay the image.

[0132] S62 Operational Closed-Loop and Adaptive Optimization During long-term operation, the system continuously records the geometric consistency index and the number of corrections of the generated results. When a certain type of scene or structure repeatedly triggers the correction mechanism, the system automatically adjusts the geometric constraint parameters in the generation process to make subsequent generation processes more stable.

[0133] Through the aforementioned closed-loop mechanism, the system can gradually optimize its generation behavior under different scenario conditions, thereby improving its overall robustness and engineering adaptability.

[0134] The overall operation flow and engineering-level closed-loop description of this embodiment are as follows: Based on the aforementioned steps, the overall operation flow of this embodiment in a real engineering system is as follows: In the first phase, the system performs input acquisition and task initialization. By acquiring visible view images, depth information, specular area masks, and camera parameters, the basic configuration for the reflection generation task is completed, and data consistency correction ensures the uniformity of input data in space and scale.

[0135] In the second stage, the system performs explicit 3D geometric modeling on the input scene. By performing structural analysis on the depth information, a 3D geometric representation of the scene is constructed, and planar parameter analysis is performed on the mirror area to clarify the position, orientation, and effective range of the mirror in space.

[0136] In the third stage, the system constructs a virtual viewpoint based on the mirror's geometric parameters and the camera's spatial relationship. The position and orientation of the virtual viewpoint are determined through geometric symmetry transformations, and a corresponding spatial projection relationship is established to ensure that the virtual viewpoint strictly conforms to the laws of real physical reflection.

[0137] In the fourth stage, the system performs coarse geometric rendering on the 3D scene structure under the virtual viewpoint, generating a virtual viewpoint rendering result with correct structure but simplified texture, and encapsulates it into a unified geometric intermediate modality, which is used as a strong geometric prior for subsequent generation processes.

[0138] In the fifth stage, the system performs specular reflection generation under geometric intermediate mode constraints. The specular reflection generation process is carried out in stages: first, the overall spatial structure is determined, then details are gradually added, and the process is applied only to the specular area to ensure that the non-reflective areas retain their original content.

[0139] In the sixth stage, the system performs geometric consistency verification on the generated results. By comparing the structural relationship between the generated reflection results and the virtual viewpoint geometric rendering, the consistency of perspective, scale, and occlusion is detected, and local feedback corrections are triggered when necessary.

[0140] In the seventh stage, after the generated results pass the consistency verification, the system outputs the final mirror reflection generation result and integrates the result into the downstream application system. At the same time, it records the key indicators during the operation for subsequent adaptive optimization.

[0141] Through the orderly execution of the above stages, this embodiment forms a complete, closed, and repeatable engineering-level reflection generation process.

[0142] Unlike existing reflection methods that only perform one-way generation, this embodiment introduces an explicit generation-verification-correction closed-loop mechanism at the system level.

[0143] In this closed-loop mechanism, the virtual viewpoint geometric rendering result serves both as a priori input before generation and as a verification benchmark after generation. If the generation process deviates from the geometric projection result, the system can promptly detect the problem through consistency detection and correct it through local regeneration, rather than relying on manual intervention or overall recalculation.

[0144] This closed-loop mechanism effectively prevents the accumulation of generation errors in complex scenarios and significantly improves the stability of the system under long-term operation or high-complexity input conditions.

[0145] The feasibility and system expansion of this embodiment are described below: The method described in this embodiment has good feasibility and scalability in engineering implementation: The modules are connected through clear data interfaces and intermediate representations, which facilitates modular implementation and independent optimization of the system; the explicit 3D geometric modeling and virtual viewpoint rendering can be implemented using mature geometric calculation and rendering technologies, without relying on specific training data; The decoupled design of the generation module and the geometry module allows the generation model to be replaced or upgraded without changing the overall architecture; Geometric consistency verification and feedback correction mechanisms enable the system to automatically correct errors, making it suitable for deployment in real-world application scenarios with high stability requirements.

[0146] Therefore, this embodiment is not only suitable for single-reflection generation tasks, but also for deployment in systems that require long-term operation, such as augmented reality, spatial reconstruction, and robot vision.

[0147] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A spatial intelligent hybrid reflection generation system integrating explicit three-dimensional geometric projection and implicit diffusion generation, characterized in that, include: Spatial Reflection Data Acquisition and Preprocessing Module: Used to provide multi-source spatial reflection sample input data for the system training and inference phases; Explicit 3D Geometric Modeling and Mirror Parameter Resolution Module: Used to recover or resolve the 3D geometric structure of the scene from the input data, and to explicitly model the geometric properties of the mirror in space; Virtual viewpoint construction and geometric coarse rendering module: used to construct virtual camera viewpoints under explicit 3D geometric constraints and generate corresponding virtual viewpoint geometric rendering results; A depth-based bi-branch diffusion generation module is used to generate specular reflection content under geometric prior constraints. Geometric Consistency Constraint Module for Reflection Results: Used to perform geometric consistency verification and structural constraints on the specular reflection results obtained by the dual-branch diffusion generation module; The phased training and model stabilization module is used to systematically train and stabilize the bibranch diffusion generation model, ensuring that the bibranch diffusion generation model maintains its effective response to geometric priors under different complexity scenarios. Inference Deployment and Cross-Scene Adaptation Module: Used to deploy the trained spatial intelligent hybrid reflection generation system to the actual application environment and achieve the ability to adapt to different scene conditions; Each module achieves hierarchical transmission and bidirectional feedback through spatial geometric information flow and generation constraint information flow, thereby forming a spatial intelligent hybrid reflection generation system that combines generation capability and spatial consistency guarantee mechanism.

2. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The spatial reflection data acquisition and preprocessing module includes: Spatial Data Unified Calibration Unit: Used to perform unified coordinate system calibration on the image, depth information, mirror area and camera parameters in the input data to ensure that subsequent geometric modeling and virtual viewpoint calculation have a consistent reference framework; Reflection region resolution unit: used to automatically or semi-automatically identify mirror regions and generate corresponding reflection masks, providing basic input for virtual viewpoint construction and constraint generation; Geometric Auxiliary Information Processing Unit: Used to extract or estimate the spatial scale, relative positional relationships, and occlusion information of the scene from the input data, in order to support subsequent explicit 3D geometric modeling.

3. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The explicit 3D geometric modeling and mirror parameter analysis module includes: 3D spatial structure modeling unit: used to reconstruct the spatial position, scale and orientation of objects in a scene based on depth maps, point clouds or external 3D information, forming a 3D geometric representation that can be used for projection calculation; Mirror plane parameter analysis unit: used to perform parametric modeling of the mirror area, analyze the normal vector, position and boundary range of the mirror plane for subsequent virtual camera viewpoint calculation; Real camera parameter acquisition unit: used to acquire or estimate the intrinsic and extrinsic parameters of the real camera, providing a basis for constructing the virtual camera viewpoint.

4. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The virtual viewpoint construction and geometry coarse rendering module includes: Virtual camera viewpoint calculation unit: used to calculate the mirror symmetry relationship based on the mirror plane parameters and the real camera pose, and to construct a virtual camera viewpoint and its projection matrix that conforms to the laws of physical reflection; Virtual viewpoint geometry rendering unit: used for fast geometry rendering of the scene from a virtual viewpoint, generating a 2D virtual viewpoint rendering map that emphasizes object outlines, occlusion relationships and perspective scaling information; Geometric prior encoding unit: used to encode the virtual viewpoint rendering map into a geometric prior feature form that can be used by the diffusion generation model, for structural constraints in the subsequent diffusion generation process.

5. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The depth-based dual-branch diffusion generation module adopts a dual-branch diffusion generation architecture, including: Visible view texture and semantic branch: used to extract local texture features and semantic representations from input images and depth information, providing a visual detail basis for reflection generation; Virtual viewpoint geometry prior branch: used to encode the global geometry and perspective relationships contained in the virtual viewpoint rendering graph; During the diffusion denoising process, the system continuously injects geometric priors through a cross-branch fusion mechanism, so that the generation process always follows the predetermined spatial structure while gradually refining the texture.

6. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The geometric consistency constraint module for reflection results includes: Geometric consistency detection unit: Based on the correspondence between the virtual viewpoint rendering map and the final generated reflection image, it performs consistency detection on key geometric elements; Structural conflict identification unit: When a significant structural conflict is detected in the generated reflection content, the system marks the conflict area and generates a corresponding constraint feedback signal; Consistency Feedback Modulation Unit: Used to feed back the geometric consistency detection results to the depth-condition-based bi-branch diffusion generation module. During the generation process, local redrawing or condition weight adjustment is performed so that the diffusion generation model more strongly follows the geometric prior in subsequent iterations, thereby gradually converging to a structurally stable reflection result.

7. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The phased training and model stabilization module includes: Training phase division unit: used to divide the training process of the diffusion generation model into several phases according to the complexity of the input scene; each phase adopts different geometric prior weights and generative degrees of freedom configurations, so that the diffusion generation model gradually learns to balance geometric constraints and generative capabilities under complex spatial conditions; Geometric constraint weight scheduling unit: used to dynamically adjust the influence weight of virtual viewpoint geometric prior in the diffusion generation model during training, so that the diffusion generation model relies more on geometric structure in the early stage and gradually improves texture and detail generation ability in the later stage, thereby avoiding overfitting to geometric contours or texture details. Model stability monitoring unit: Used to continuously monitor the generation stability indicators of the diffusion generation model in the validation set or inference stage. When the performance of the diffusion generation model shows a deterioration trend, it automatically triggers the training parameter adjustment or rollback mechanism.

8. The spatial intelligent hybrid reflection generation system according to claim 1, characterized in that, The inference deployment and cross-scenario adaptation module includes: Inference process control unit: During the actual inference stage, the system automatically determines whether to enable the virtual viewpoint rendering module and its accuracy level based on the input scene conditions, thereby dynamically balancing real-time performance and generation quality. Scene Adaptive Adjustment Unit: Used to dynamically adjust the geometric prior injection strength and diffusion generation steps for different application scenarios, so that the system can run stably under different hardware and environmental conditions; Cross-scene generalization support unit: It is used to gradually optimize the system parameter configuration through long-term statistical analysis of the consistency index of the generated results, so that the model can maintain reasonable generation behavior under unseen spatial layout and reflection conditions.

9. A spatial intelligent hybrid reflection generation method that integrates explicit three-dimensional geometric projection and implicit diffusion generation, characterized in that, include: By acquiring visible view images, depth information, mirror area masks, and camera parameters, the basic configuration for the reflection generation task is completed, and data consistency correction is used to ensure the uniformity of input data in space and scale. Explicit 3D geometric modeling is performed on the input scene: by performing structural analysis on the depth information, a 3D geometric representation of the scene is constructed, and planar parameter analysis is performed on the mirror area to clarify the position, orientation and effective range of the mirror in space; Based on the geometric parameters of the mirror and the spatial relationship of the camera, a virtual viewpoint is constructed: the position and orientation of the virtual viewpoint are determined by geometric symmetry transformation, and the corresponding spatial projection relationship is established to ensure that the virtual viewpoint conforms to the real physical reflection law; Perform geometric coarse rendering on the 3D scene structure under the virtual viewpoint to generate a virtual viewpoint rendering result with correct structure but simplified texture, and encapsulate it into a unified geometric intermediate modality to be used as a strong geometric prior for subsequent generation processes. Mirror reflection generation is performed under the geometric intermediate mode constraint: the mirror reflection generation process is carried out in stages. First, the overall spatial structure is determined, and then the details are gradually added. It only applies to the mirror area to ensure that the non-reflective area retains the original content. Perform geometric consistency verification on the generated results: detect the consistency of perspective, scale and occlusion by comparing the structural relationship between the generated reflection results and the virtual viewpoint geometric rendering; Once the generated result passes the consistency verification, the final specular reflection generation result is output and integrated into the downstream application system. At the same time, key indicators during the operation are recorded for subsequent adaptive optimization.

10. The spatial intelligent hybrid reflection generation method according to claim 9, characterized in that, A virtual viewpoint constrained diffusion generation algorithm is used to perform geometric coarse rendering of the 3D scene structure under a virtual viewpoint. The virtual viewpoint constraint diffusion generation algorithm includes the following steps: The visible view image, depth information, specular mask, and camera parameters are obtained as input. Based on the mirror plane parameters and the actual camera pose, the virtual camera viewpoint is calculated, and the corresponding virtual viewpoint geometric rendering is generated. The virtual viewpoint rendering map is encoded as geometric prior features and used as a conditional input to the geometric branch of the diffusion generation model; In the diffusion denoising process, geometric prior features are fused with visible view features at each time step to guide the gradual generation of the reflection region.

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