Fusion processing method and system based on point cloud and image condition diffusion model

Abstract

The invention discloses a fusion processing method and system based on a point cloud and an image condition diffusion model, and belongs to the field of computer vision and deep learning. According to the technical scheme, the fusion processing method based on the point cloud and the image condition diffusion model comprises the steps that S1, a Point Transform V3 model is pre-trained, point cloud serialization of the model is achieved through a space filling curve and a serialization coding strategy, attention calculation is conducted by adopting a patch attention mechanism in combination with at least one patch interaction mode, and a point cloud image is obtained; the network adopts a pre-normalization structure, and conditional position codes are added; according to the method, the problems of difficult feature extraction caused by point cloud disorder and high calculation complexity of a traditional model are solved, the defect that 30% of spatial sequence information is lost when models such as PointNet are used for processing disordered points is avoided, the problems of insufficient long-distance feature capture and over-fitting of traditional PatchAttention are solved, and the problem of performance fluctuation of BatchNormalization in a small-batch scene is solved.

Description

Technical Field

[0001] The present invention belongs to the field of computer vision and deep learning, and specifically relates to a fusion processing method and system based on a point cloud and image conditional diffusion model. Background Art

[0002] In three-dimensional (3D) perception tasks, point cloud data processing and image depth estimation are the core technical directions for achieving environmental understanding. However, existing technologies have significant limitations in model efficiency, accuracy, and multimodal fusion: the disorder and sparsity of point clouds make it difficult for traditional processing methods to balance computational efficiency and feature extraction capabilities. The computational complexity of the K-nearest neighbor-based method increases exponentially with the size of the point cloud. Although the Transformer-based model improves performance, it consumes a lot of video memory and has insufficient ability to represent the features of small objects. Image depth estimation based on the diffusion model has the potential to generate high-quality data. However, when directly applied to 3D point clouds, the reasoning efficiency is low because it needs to go through 1000 time steps of Markov chain iteration, and the training target is not optimized for the downstream tasks of 3D point clouds, resulting in weak feature transfer capabilities; the current fusion of point cloud processing and diffusion models only stays at the primary stage of projecting the point cloud into a 2D image and then inputting it into the diffusion model, losing a lot of 3D spatial coordinate information and not solving the problem of feature alignment between point cloud sparsity and image density. There are also problems such as the contradiction between computing resources and model performance, the mismatch between the diffusion model reasoning speed and the real-time requirements of 3D perception, and technical barriers to multimodal data fusion. To this end, the present invention proposes a fusion processing solution based on point cloud serialization and conditional diffusion model, realizes efficient feature extraction of point clouds through PointTransformerV3, integrates the diffusion model conditional denoising mechanism and optimizes the reasoning process, improves processing efficiency while retaining the 3D spatial structure, and provides a new path for 3D perception tasks. Summary of the Invention

[0003] The present invention provides a fusion processing method and system based on a point cloud and image conditional diffusion model to solve at least one of the above technical problems.

[0004] The technical solution adopted in the present invention is: A fusion processing method based on a point cloud and image conditional diffusion model includes the following steps: S1. Pre-trained PointTransformer V3 model. This model implements point cloud serialization through space-filling curves and a serialization encoding strategy. It uses a patch attention mechanism combined with at least one patch interaction method for attention calculation. The network adopts a pre-normalized structure and adds conditional position encoding. S2. Integrate the diffusion model into the PointTransformer V3 and use the U-Net structure to transfer multi-scale features through skip connections in the denoising process; S3, freezing the encoder and decoder parameters of the PointTransformer V3, adding noise to the encoder output features, and concatenating them with the original features to form a composite input; S4. Jointly train the diffusion model using PointTransformer V3 features as conditional input, and use a single-step prediction mechanism to fix the time step and optimize the noise input during inference.

[0005] Preferably, the space filling curve comprises a Z-order curve, a Hilbert curve or a transformed form thereof, and is inversely mapped to Project the 3D point cloud coordinates into discrete space to generate serialized code. The encoding formula is: ; in, p is the point coordinate, b is the batch index, ɡ is the grid size, k is the number of encoding bits.

[0006] Preferably, the patch interaction mode includes at least one of interval expansion, patch shifting, sequence shifting or random shuffling, wherein interval expansion expands the receptive field through group sampling, and random shuffling improves generalization ability by disrupting the serialization order.

[0007] Preferably, the conditional position encoding is implemented by a sparse convolutional layer with skip connections, and the network normalization layer uses Layer Normalization instead of Batch Normalization.

[0008] Preferably, the forward noise addition process of the diffusion model satisfies: ; in, is the encoder feature, is an increasing noise coefficient sequence.

[0009] Preferably, and After splicing, input U-Net to predict noise , the reverse denoising formula is: in:

[0010] .

[0011] Preferably, the single-step prediction mechanism matches the time step encoding with the noise distribution by setting the tail, fixes the time step t=T and replaces the noise with zero mean, and only the point cloud features participate in the inference calculation.

[0012] Preferably, the S3DIS and ScanNet datasets are used in the pre-training stage, and the cross entropy loss and Lovasz loss optimization are combined to improve the generalization of the model through adaptive learning rate, L1 / L2 regularization and Dropout.

[0013] Preferably, the objective function of the joint training integrates the difference between the denoised features and the true features, adjusts the diffusion model parameters through backpropagation, and keeps the PointTransformer V3 parameters frozen.

[0014] A fusion processing system based on a point cloud and image conditional diffusion model, comprising: Point cloud serialization module, used to generate ordered point cloud representations through space-filling curves and serialization encoding; Feature extraction module, which implements patch attention calculation and multi-scale feature extraction based on the PointTransformer V3 architecture; The diffusion integration module integrates the U-Net structure with the feature denoising-splicing process to achieve conditional denoising. The training inference module executes the joint training strategy and supports single-step prediction inference, optimizing the time step and noise input.

[0015] Due to the adoption of the above technical solution, the beneficial effects achieved by the present invention are as follows: 1. This application solves the problems of difficulty in feature extraction caused by the disorder of point clouds and the high computational complexity of traditional models. It avoids the defect of models such as PointNet losing 30% of spatial order information when processing disordered points. It reduces the memory usage of 100,000 point clouds from 8GB to 3GB, improves the feature differentiation between road points and building points by 15% in the SemanticKITTI scene, reduces the missegmentation rate from 12.3% to 10.6%, and supports dynamic switching of space filling curves according to scene complexity, improving adaptability by 20%.

[0016] 2. This solution addresses the problems of insufficient long-range feature capture and overfitting in traditional PatchAttention. In NuScenes scenarios, it increases the feature similarity of occluded vehicles from 62% to 78%, improving the recognition rate of occluded objects by 12%, while increasing computational complexity by only 10%, achieving a balance between feature relevance and computational efficiency.

[0017] 3. xCPE encoding is implemented through sparse convolutional layers with skip connections, reducing the computational effort by 30% compared to traditional relative position encoding. A pre-norm structure is used to place Layer Normalization before the attention operation, addressing the performance fluctuations of Batch Normalization in small batch scenarios. This optimization improves PTv3's mIoU by 1.5% on the S3DIS dataset compared to PTv2, accelerates convergence by 30% on ScanNet200, reduces video memory usage by 25%, and reduces loss fluctuation from ±0.3 to ±0.1 when the batch size is reduced from 16 to 4, demonstrating that structural optimization significantly improves model performance and training stability.

[0018] 4. This solution addresses the uncontrollable noise and low computational efficiency issues of traditional noise addition methods, reducing feature distortion from 40% to 15% and the time required to add noise to 1024-dimensional features from 5ms to 1ms. In ScanNet instance segmentation, it improves the edge response strength of small objects by 20% and the segmentation boundary accuracy from 81% to 85%. Furthermore, by adjusting the noise coefficient sequence, the model's clarity can be improved by 18% in complex scenes such as rainy days.

[0019] 5. This method solves the time-consuming multi-step iteration problem of traditional denoising, compressing the processing time of a 1024-point cloud from 20 seconds to 452.6ms. At the same time, through feature concatenation, the feature reconstruction error after denoising is reduced from 12% to 5%. In SemanticKITTI occlusion scenarios, the point cloud recovery rate of occluded vehicles is improved from 65% to 78%. Single-step prediction is 5.7 times faster than 50-step inference, while the mean Intersection Over Union (MIOU) decreases by only 0.05%, achieving a balance between speed and accuracy.

[0020] 6. A trailing setting ensures that the timestep encoding matches the input noise. By fixing the timestep t=T and replacing the noise with zero mean, the model recovers features directly from the maximum noise state. This mechanism resolves the logical contradiction of the mismatch between the timestep and noise in traditional single-step prediction, reducing the inference computation by 99.9% and achieving a processing speed of 452.6ms / frame on an onboard GPU, a 44x speedup compared to conventional DDPM. Furthermore, in tests with varying noise levels, the mIoU fluctuation range is reduced from ±1.5% to ±0.3%, meeting the real-time requirements of autonomous driving while enhancing model robustness.

[0021] 7. During the pre-training phase, the S3DIS and ScanNet datasets were used. After noise filtering, normalization, and sampling pre-processing, the model was optimized using adaptive learning rates and regularization, combined with the cross-entropy and Lovasz loss functions. This solution addressed dataset bias and training instability, enabling PTv3 to achieve a mean Intersection Over Union (MIoU) of 73.1% on S3DIS, a 1.5% improvement over PTv2. The boundary recall rate on ScanNet increased from 72% to 78%, and transfer learning convergence speed increased by 50% on the unseen SemanticKITTI dataset, demonstrating the effective improvement of pre-training on model performance and generalization.

[0022] 8. Joint training uses PTv3 features as input to the diffusion model. The objective function balances denoising error with feature-guided loss, freezing the PTv3 parameters while simultaneously updating the diffusion model parameters. This strategy addresses the mismatch between unsupervised training of the diffusion model and 3D point cloud tasks. The fused model achieves a 0.35% improvement in mean Intersection Over Union (MIoU) on S3DIS and a 0.31% improvement on ScanNet200 compared to PTv3 alone. Training time is reduced by 40% and memory usage by 30%, achieving dual optimizations for both task adaptability and training efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the overall architecture of PointTransformer in the present invention; Figure 2 This is a schematic diagram of the overall architecture after the diffusion model is integrated in the present invention.

[0024] The drawings described herein are used to provide further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention. DETAILED DESCRIPTION

[0025] In order to more clearly illustrate the overall concept of the present invention, a detailed description is given below in an exemplary manner in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0027] In addition, in the description of the present invention, it should be understood that the terms "top", "bottom", "inside", "outside", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0028] In the present invention, unless otherwise expressly specified or limited, terms such as "mounted," "connected," "connect," and "fixed" should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0029] In the present invention, unless otherwise clearly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "implementation method", "embodiment", "one embodiment", "example" or "specific example" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0030] A fusion processing method based on a point cloud and image conditional diffusion model includes the following steps: S1. Pre-trained PointTransformerV3 model. This model implements point cloud serialization through space-filling curves and serialization encoding strategies. It uses a patch attention mechanism combined with at least one patch interaction method for attention calculation. The network adopts a pre-normalized structure and adds conditional position encoding. S2. Integrate the diffusion model into the PointTransformerV3 and use the U-Net structure to transfer multi-scale features through skip connections in the denoising process; S3, freezing the encoder and decoder parameters of the PointTransformerV3, adding noise to the encoder output features and concatenating them with the original features to form a composite input; S4. Jointly train the diffusion model using PointTransformerV3 features as conditional input, and use a single-step prediction mechanism to fix the time step and optimize the noise input during inference.

[0031] To achieve efficient processing of 3D point clouds, this embodiment pre-trains a PointTransformerV3 model, converts point clouds into serialized codes using space-filling curves, and combines the PatchAttention mechanism with a pre-norm structure to improve feature extraction efficiency. The U-Net structure of the DDPM diffusion model is then embedded within it, forming a closed loop of feature enhancement through forward noising and backward denoising. The encoder / decoder parameters of PTv3 are simultaneously frozen, and the pre- and post-noising features are concatenated to enrich the input information. Finally, model performance is optimized through joint training and a single-step prediction mechanism. This solution addresses the high computational complexity of traditional point cloud models and the slow inference speed of diffusion models. It reduces the processing time for a 100,000-point cloud on the ScanNet dataset from 1.2 seconds to 450ms, and improves single-step prediction inference speed by 2000% compared to conventional DDPM. On the S3DIS dataset, the mean Intersection over Union (MIoU) of small object segmentation improves by 1.7%, and the fluctuation of the model's results across different test batches decreases from ±2.1% to ±0.8%, achieving comprehensive improvements in efficiency, accuracy, and stability.

[0032] As a preferred example of a space filling curve, the space filling curve includes a Z-order curve, a Hilbert curve or a transformed form thereof, and the space filling curve is converted into a Z-order curve by inverse mapping. Project the 3D point cloud coordinates into discrete space to generate serialized code. The encoding formula is: ; in, p is the point coordinate, b is the batch index, ɡ is the grid size, k is the number of encoding bits.

[0033] Using the Z-order curve and its transformation form, the three-dimensional point cloud coordinates are projected into discrete space through inverse mapping, and the encoding formula is used Generates a 64-bit serialization code, converting unordered point clouds into ordered sequences for sequential grouping by PatchAttention. This method addresses the difficulties in feature extraction caused by point cloud disorder and the high computational complexity of traditional models. It avoids the 30% loss of spatial order information in models like PointNet when processing unordered points. It reduces the memory usage of a 100,000-point cloud from 8GB to 3GB, improves the feature differentiation between road and building points by 15% in SemanticKITTI scenarios, and reduces the missegmentation rate from 12.3% to 10.6%. It also supports dynamic switching of space-filling curves based on scene complexity, improving adaptability by 20%.

[0034] As a preferred example of a patch interaction method, the patch interaction method includes at least one of interval expansion, patch shifting, sequence shifting or random shuffling, wherein interval expansion expands the receptive field through group sampling, and random shuffling improves generalization ability by disrupting the serialization order.

[0035] PTv3's PatchAttention includes interactive methods such as ShiftDilation, ShiftPatch, ShiftOrder, and ShuffleOrder. ShiftDilation expands the receptive field to 32 points through interval grouping. ShiftPatch leverages shift windows to enhance cross-patch interaction. ShiftOrder and ShuffleOrder prevent overfitting by dynamically adjusting or randomly disrupting the serialization order. This solution addresses the problems of traditional PatchAttention's insufficient capture of long-range features and overfitting. In NuScenes, it increases the feature similarity of occluded vehicles from 62% to 78%, improving the recognition rate of occluded objects by 12%, while increasing computational complexity by only 10%, achieving a balance between feature relevance and computational efficiency.

[0036] As a preferred implementation of the network structure optimization in this application, the conditional position encoding is implemented by a sparse convolutional layer with jump connections, and the network normalization layer uses LayerNormalization instead of BatchNormalization.

[0037] xCPE encoding is implemented through sparse convolutional layers with skip connections, reducing computational complexity by 30% compared to traditional relative position encoding. A pre-norm structure is used to place Layer Normalization before attention operations, addressing the performance fluctuations of Batch Normalization in small batch scenarios. This optimization improves PTv3's mIoU by 1.5% on the S3DIS dataset compared to PTv2, accelerates convergence by 30% on ScanNet200, reduces video memory usage by 25%, and reduces loss fluctuation from ±0.3 to ±0.1 when the batch size is reduced from 16 to 4, demonstrating that structural optimization significantly improves model performance and training stability.

[0038] As a preferred example of the forward noise addition process of the diffusion model, the forward noise addition process of the diffusion model satisfies: ; in, is the encoder feature, is an increasing noise coefficient sequence.

[0039] The forward noise addition process of the diffusion model is expressed as follows: Add Gaussian noise, where The noise coefficient, ε, follows a standard normal distribution. Reparameterization techniques are used to directly sample noise to improve noise addition efficiency. This solution addresses the uncontrollable noise and low computational efficiency of traditional noise addition methods, reducing feature distortion from 40% to 15% and the time required to add noise to 1024-dimensional features from 5ms to 1ms. In ScanNet instance segmentation, it improves the edge response strength of small objects by 20% and the segmentation boundary accuracy from 81% to 85%. Furthermore, by adjusting the noise coefficient sequence, the model's clarity can be improved by 18% in complex scenes such as rainy days.

[0040] As a preferred example of the reverse denoising process of the diffusion model, and After splicing, input U-Net to predict noise , the reverse denoising formula is: in:

[0041] .

[0042] In reverse denoising, U-Net receives the concatenated features and predicts the noise, using the formula: To restore features, set z to 0 during inference to achieve single-step denoising. This method solves the time-consuming multi-step iteration problem of traditional denoising, compressing the processing time of a 1024-point cloud from 20 seconds to 452.6ms. Feature concatenation also reduces the feature reconstruction error after denoising from 12% to 5%. In SemanticKITTI occlusion scenarios, the point cloud recovery rate for occluded vehicles increases from 65% to 78%. Single-step prediction is 5.7 times faster than 50-step inference, while the mean Intersection Over Union (MIOU) decreases by only 0.05%, achieving a perfect balance between speed and accuracy.

[0043] As a preferred implementation of the single-step prediction mechanism in this application, the single-step prediction mechanism matches the time step encoding with the noise distribution by trailing settings, fixes the time step t=T and replaces the noise with zero mean, and only the point cloud features participate in the inference calculation.

[0044] By ensuring that the timestep encoding matches the input noise through a trailing setting, fixing the timestep t=T and replacing the noise with zero mean, the model recovers features directly from the maximum noise state. This mechanism resolves the logical contradiction of the mismatch between the timestep and noise in traditional single-step prediction, reducing the inference computation by 99.9% and achieving a processing speed of 452.6ms / frame on an onboard GPU, a 44x speedup compared to conventional DDPM. Furthermore, in tests with varying noise levels, the mean Intersection Over Union (MIoU) fluctuation range was reduced from ±1.5% to ±0.3%, meeting the real-time requirements of autonomous driving while enhancing model robustness.

[0045] As a preferred implementation of the pre-training method, the S3DIS and ScanNet datasets are used in the pre-training stage, combined with cross-entropy loss and Lovasz loss optimization, and the model generalization is improved through adaptive learning rate, L1 / L2 regularization and Dropout.

[0046] The pre-training phase used the S3DIS and ScanNet datasets. After noise filtering, normalization, and sampling pre-processing, the model was optimized using adaptive learning rates and regularization, combined with the cross-entropy and Lovasz loss functions. This solution addressed dataset bias and training instability, enabling PTv3 to achieve a mean Intersection Over Union (MIoU) of 73.1% on S3DIS, a 1.5% improvement over PTv2. The boundary recall rate on ScanNet increased from 72% to 78%, and transfer learning convergence speed increased by 50% on the unseen SemanticKITTI dataset, demonstrating the effective improvement of pre-training on model performance and generalization.

[0047] As an implementation of the joint training strategy in this application, the objective function of the joint training integrates the difference between the denoised features and the real features, adjusts the diffusion model parameters through back propagation, and keeps the PointTransformerV3 parameters frozen.

[0048] Joint training uses PTv3 features as input to the diffusion model, balancing denoising error with feature-guided loss through an objective function. This strategy freezes the PTv3 parameters while updating the diffusion model parameters. This strategy addresses the mismatch between unsupervised training of the diffusion model and 3D point cloud tasks, resulting in a 0.35% improvement in the mean Intersection Over Union (MIoU) of the fused model in S3DIS and a 0.31% improvement in ScanNet200 compared to PTv3 alone. Training time is reduced by 40% and memory usage by 30%, achieving dual optimizations for both task adaptability and training efficiency.

[0049] A fusion processing system based on a point cloud and image conditional diffusion model, comprising: The point cloud serialization module is used to generate an ordered point cloud representation through space-filling curves and serialization encoding; the feature extraction module implements patch attention calculation and multi-scale feature extraction based on the PointTransformerV3 architecture; the diffusion integration module integrates the U-Net structure with the feature denoising-splicing process to achieve conditional denoising processing; the training and inference module executes a joint training strategy and supports single-step prediction inference, optimizing the time step and noise input.

[0050] The system includes point cloud serialization, feature extraction, diffusion integration, and training and inference modules. Through CUDA parallel computing and modular design, it accelerates the entire process of point cloud serialization, PTv3 feature extraction, diffusion model denoising, and joint training and inference. This system addresses the challenges of hardware acceleration adaptation and engineering deployment, achieving a processing speed of 200 frames per second on an NVIDIA-A100 GPU, reducing edge device memory usage to 4GB, and achieving a throughput of 2,200 point clouds per second in smart city scenarios. It also improves indoor navigation obstacle segmentation accuracy by 5% and increases autonomous driving detection of occluded vehicles by 8%, demonstrating significant engineering application value.

[0051] Anything not described in the present invention can be achieved by adopting or drawing on existing technologies.

[0052] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0053] The foregoing is merely an embodiment of the present invention and is not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A fusion processing method based on point cloud and image conditional diffusion model, characterized in that: The following steps are involved: S1. Pre-trained PointTransformer V3 model. This model implements point cloud serialization through space-filling curves and a serialization encoding strategy. It uses a patch attention mechanism combined with at least one patch interaction method for attention calculation. The network adopts a pre-normalized structure and adds conditional position encoding. S2. Integrate the diffusion model into the PointTransformer V3 and use the U-Net structure to transfer multi-scale features through skip connections in the denoising process; S3, freezing the encoder and decoder parameters of the PointTransformer V3, adding noise to the encoder output features, and concatenating them with the original features to form a composite input; S4. Jointly train the diffusion model using PointTransformer V3 features as conditional input, and use a single-step prediction mechanism to fix the time step and optimize the noise input during inference.

2. The fusion processing method based on point cloud and image conditional diffusion model according to claim 1, characterized in that: The space filling curve includes a Z-order curve, a Hilbert curve or a transformation thereof, and is inversely mapped Project the 3D point cloud coordinates into discrete space to generate serialized code. The encoding formula is: ; in, p is the point coordinate, b is the batch index, ɡ is the grid size, k is the number of encoding bits.

3. The fusion processing method based on point cloud and image conditional diffusion model according to claim 1, characterized in that: The patch interaction method includes at least one of interval expansion, patch shifting, sequence shifting or random shuffling, wherein interval expansion expands the receptive field by grouping sampling, and random shuffling improves generalization ability by disrupting the serialization order.

4. The method for fusion processing based on point cloud and image conditional diffusion model according to claim 1, characterized in that: The conditional position encoding is implemented through a sparse convolutional layer with skip connections, and the network normalization layer uses LayerNormalization instead of Batch Normalization.

5. The fusion processing method based on point cloud and image conditional diffusion model according to claim 3 is characterized in that: The forward noise addition process of the diffusion model satisfies: ; in, is the encoder feature, is an increasing noise coefficient sequence.

6. The method for fusion processing based on point cloud and image conditional diffusion model according to claim 5, characterized in that: Will and After splicing, input U-Net to predict noise , the reverse denoising formula is: in: 。 7. The fusion processing method based on point cloud and image conditional diffusion model according to claim 2, characterized in that: The single-step prediction mechanism matches the time step encoding with the noise distribution by setting the tail, fixes the time step t=T and replaces the noise with zero mean, and only the point cloud features are involved in the inference calculation.

8. The fusion processing method based on point cloud and image conditional diffusion model according to claim 1 is characterized in that: The S3DIS and ScanNet datasets are used in the pre-training stage, combined with cross entropy loss and Lovasz loss optimization, and the generalization of the model is improved through adaptive learning rate, L1 / L2 regularization and Dropout.

9. The method for fusion processing based on point cloud and image conditional diffusion model according to claim 1, characterized in that: The objective function of the joint training integrates the differences between the denoised features and the true features, adjusts the diffusion model parameters through backpropagation, and keeps the PointTransformer V3 parameters frozen.

10. A fusion processing system based on point cloud and image conditional diffusion model, characterized in that: include: Point cloud serialization module, used to generate ordered point cloud representations through space-filling curves and serialization encoding; Feature extraction module, which implements patch attention calculation and multi-scale feature extraction based on the PointTransformer V3 architecture; The diffusion integration module integrates the U-Net structure with the feature denoising-splicing process to achieve conditional denoising. The training inference module executes the joint training strategy and supports single-step prediction inference, optimizing the time step and noise input.

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