187 results about "Gauss point" patented technology

The invention discloses a three-dimensional Gaussian sputtering scene reconstruction method based on structure perception refined Gaussian, and aims to solve the problems of Gaussian drift, edge blur, structure artifacts and the like of a reconstruction model due to the fact that sparse point cloud contains outliers, Gaussian morphology and normal are mismatched and a multi-dimensional optimization target is lacked in an existing three-dimensional Gaussian sputtering reconstruction method. A key frame is extracted by collecting target scene video data, sparse three-dimensional point clouds are reconstructed by using an SfM algorithm, a depth map and a normal map are generated through a Lotus model, three-dimensional Gaussian distribution is initialized after the sparse point clouds are filtered, a Gaussian covariance matrix is adjusted by using a normal consistency regular term, and the sparse point clouds are extracted. And after structure attribute analysis is carried out, a comprehensive scoring function is constructed to screen Gaussian points, and finally, a combined training framework including luminosity, normal consistency and structure continuity loss is adopted to optimize and generate a three-dimensional Gaussian scene model. The method is mainly applied to the field of three-dimensional reconstruction and multi-view rendering, and scene reconstruction precision and geometric consistency can be improved.

The invention relates to the technical field of computer vision, in particular to a point projection type three-dimensional reconstruction and segmentation method and system based on semi-Gaussian pruning. The method comprises the following steps: respectively obtaining an SFM point cloud and a consistency label mask of a cross-view label based on an obtained multi-view image; initializing the obtained SFM point cloud into an identity semi-Gaussian point cloud, and performing rendering optimization by using a differentiable renderer; densifying the initial sparse point cloud by using a localized semi-Gaussian point management method, and identifying a local error region for resetting and repairing; using the obtained consistency label mask to supervise Gaussian identity feature learning by using cross entropy loss, and using unsupervised 3D regularization loss to force spatially adjacent gauss to maintain identity consistency; according to the method, the identity coding semi-Gaussian kernel method is adopted, the inherent representation fuzziness of a single opacity formula is eliminated, and the positive influence on the identity coding precision is generated.

Disclosed in the present invention is a three-dimensional Gaussian splatting optimization method for an unposed input. The method comprises: for an input image, predicting a ray bundle distribution by using a ray prediction model, to obtain distribution features in the form of ray bundles; on the basis of the distribution features in the form of ray bundles, calculating a camera pose; for the ray bundle distribution, performing sampling on the basis of the volumetric density of light rays, to obtain an initial spatial distribution of a three-dimensional Gaussian point cloud focused on a visual center area; for the input image, obtaining a visible shell by means of view frustum projection and object-mask computation; on the basis of the initial spatial distribution of the three-dimensional Gaussian point cloud and the visible shell, performing three-dimensional Gaussian splatting scene training, to obtain a three-dimensional scene reconstruction model satisfying a preset loss function criterion, the loss function comprising a training regularization term for a camera pose parameter. The present invention provides important scene initialization information for three-dimensional Gaussian splatting training, and significantly improves the quality and the richness of detail of the final three-dimensional structure.

The invention discloses rendering equipment based on three-dimensional Gaussian sputtering. The hardware architecture includes a memory interface, a pre-processing engine, a cardinality ordering engine, a rasterization engine, and the like. The preprocessing engine converts the three-dimensional Gaussian point into a two-dimensional representation and generates a key value pair containing a tile identifier and depth information; the cardinal number sorting engine performs staged efficient sorting through a parallel processing mechanism and a BRAM alternate working mode; and the rasterization engine performs rendering processing by adopting a full-pipeline design and an early-stage stopping mechanism. According to the method, parallel execution of depth sequencing and rasterization is achieved, memory access and calculation redundancy are reduced through optimized data flow design, an efficient and low-power-consumption hardware acceleration solution is provided for three-dimensional Gaussian sputtering rendering, and the method is particularly suitable for application scenes such as virtual reality, games and scientific visualization requiring real-time rendering.

The invention discloses a dynamic scene repairing method based on four-dimensional Gaussian sputtering, which is characterized in that efficient dynamic scene space repairing is realized based on a repairing framework of a four-dimensional Gaussian sputtering monocular video reconstruction algorithm and a video repairing model, and by counting the time frame number of Gaussian primitives in a mask area and removing Gaussian points exceeding a threshold value, the dynamic scene space repairing efficiency is improved. According to the method, high-efficiency dynamic object removal is realized, and a mask weighting loss function is introduced to guide the repair area to converge to real texture information in a subsequent time frame, so that the repair quality is improved. The invention provides a dynamic scene repair method based on four-dimensional Gaussian sputtering, and provides a repair framework based on a four-dimensional Gaussian sputtering monocular video reconstruction algorithm and a video repair model for removing and repairing a target object in a dynamic scene to realize three-dimensional repair of the dynamic scene.

The invention provides an infrared scene reconstruction and rendering method and device based on three-dimensional Gaussian splashing, and the method comprises the steps: carrying out the processing of preprocessed infrared image data through employing an SfM algorithm, obtaining a sparse three-dimensional point cloud and a camera pose, and setting a Gaussian ellipsoid at the center position of the sparse three-dimensional point cloud, so as to construct a three-dimensional Gaussian point cloud model; inputting the camera pose into a Gaussian microrasterized renderer to render the three-dimensional Gaussian point cloud model to obtain a two-dimensional image of a corresponding view angle; the two-dimensional image and the infrared image data of the camera pose are compared through a loss function, the model is trained, a trained three-dimensional Gaussian point cloud model is obtained, and the loss function comprises a temperature gradient field, the projection position of a Gaussian ellipsoid in a pixel plane under the current camera visual angle and the main axis projection direction; and processing the trained three-dimensional Gaussian point cloud model by using a three-dimensional graphic engine and a 3D Gaussian rasterization renderer to obtain an infrared simulation image. And the authenticity of reconstruction and rendering of the infrared scene is relatively high.

The invention discloses a plant three-dimensional Gaussian phenotype extraction method based on an improved parameter adaptive fast pose optimization algorithm, and the method comprises the steps: obtaining a multi-view image sequence of a plant through a camera system, estimating the pose of a camera through a global adaptive fast pose optimization algorithm, and generating a sparse three-dimensional point cloud of a scene, pose estimation errors in an agricultural scene can be effectively eliminated, the problem of error accumulation in a traditional method is avoided, and high-precision camera pose estimation is guaranteed; secondly, taking the camera pose and the sparse three-dimensional point cloud as input, converting an object in a scene into a plurality of three-dimensional Gaussian points, performing three-dimensional reconstruction based on a Gaussian representation method, and generating a dense three-dimensional point cloud, thereby solving common shielding and dynamic change problems in an agricultural scene, and improving the quality and efficiency of point cloud reconstruction; and finally, based on the obtained dense three-dimensional point cloud, phenotypic characteristics of the plants are extracted, and more accurate data support is provided for crop phenotypic analysis.

The invention relates to an editable human body reconstruction method and system based on three-dimensional Gaussian splashing, and the method converts a dynamic human body reconstruction problem into a reconstruction problem of a static template human body in a standard space, achieves the respective decoupling of the shape and the appearance through a mode of constructing the mapping of a three-dimensional Gaussian model and a parameterized human body model, and achieves the reconstruction of the human body. Human body reconstruction in an editable form is achieved, and the editable form comprises the steps that geometric editing is achieved by changing a body shape coefficient and a pose coefficient in the parameterized human body model; and replacing the vertex mapped three-dimensional Gaussian point cloud in the editable three-dimensional Gaussian model to realize texture editing. Compared with the prior art, the method solves the problem that personalized editing cannot be carried out on the human body model based on three-dimensional Gaussian splashing in the prior art.

The invention relates to the technical field of synchronous positioning and mapping, in particular to a positioning mapping method and system based on three-dimensional Gaussian splashing and a depth camera, and the method comprises the steps: collecting the image and depth information of a target scene in real time, and obtaining the data of a current frame and the corresponding Gaussian point cloud data; based on the current frame data and the corresponding Gaussian point cloud data, adopting an Adam optimization algorithm and analyzing a Jacobian matrix to estimate the camera pose of the current frame; selecting a key frame from a plurality of current frames based on the camera pose, and constructing a dynamic key frame window based on the key frame; and according to the key frame data and the corresponding Gaussian point cloud data, adding a Gaussian point cloud in an area with insufficient coverage, and carrying out iterative rendering on the key frame in the dynamic key frame window to complete the construction of the three-dimensional map. According to the invention, rapid and high-quality positioning mapping can be realized.

The invention relates to a real-time digital person generation method and system, and the method comprises the following steps: obtaining a monocular video of a target person, and extracting 3DMM information in the monocular video; performing Gaussian point initialization on the monocular video according to the 3DMM information to obtain Gaussian parameters in a standard space; extracting voice audio features in the monocular video, and inputting the voice audio features into the audio-motion model to obtain a universal face key point motion sequence; converting the universal face key point motion sequence into a target face key point motion sequence through a projection algorithm; inputting the target face key point motion sequence and the Gaussian parameters in the standard space into a face key point guided Gaussian deformation network to obtain Gaussian deformation parameters; and rendering the Gaussian deformation parameter into a corresponding video frame through a Gaussian rasterizer, thereby obtaining a digital person video of the target person. Compared with the prior art, the digital human video generation accuracy and creation flexibility are improved.

The invention relates to a three-dimensional reconstruction-oriented Gaussian point cloud lossless coding and decoding method, a product and a storage medium. The method comprises the following steps: extracting a first Gaussian point cloud corresponding to anhor data in a numpy array format at a coding side; converting the first Gaussian point cloud in the numpy array format into a first Gaussian point cloud in a ply format; meanwhile, through a Morton code-based spatial sorting method, performing Morton code sorting on geometric data and attribute data of the first Gaussian point cloud in the numpy array format, and generating a Morton sequence index table; and carrying out AVS coding on the first Gaussian point cloud in the ply format to obtain compressed data in a binary code stream form. On the decoding side, AVS decoding is carried out when the compressed data in the binary code stream form and the Morton sequence index table are received, and a second Gaussian point cloud in the ply format is obtained through reduction; performing format conversion on the second Gaussian point cloud in the ply format to obtain a second Gaussian point cloud in a numpy array format; and rearranging the attribute data and the geometric data based on the Morton sequence index table to realize one-to-one correspondence of the geometric data and the attribute data between the first Gaussian point cloud and the second Gaussian point cloud before and after coding and decoding. Compared with an existing method, the data consistency before and after coding is improved.

The invention relates to an object attitude estimation method based on scene-level semantic three-dimensional Gaussian spatter, which comprises the following steps of: firstly, constructing scene-level three-dimensional representation according to a multi-view image, and associating semantic embedding of three-dimensional Gaussian points of each target object; positioning a target mask area of a target object described by a language instruction according to the query image, and extracting each target three-dimensional Gaussian point subset through semantic matching and three-dimensional space clustering; secondly, an ICP registration method is guided through two-stage learning, and the initial 6D pose of the target object is obtained; and finally, performing cascade fine optimization by combining pose rendering and similarity comparison under disturbance to obtain a 6D pose of the target object in the target scene. According to the design scheme, target retrieval and instance extraction under open vocabularies are achieved through three-dimensional Gaussian reconstruction of semantic enhancement, and the attitude estimation precision under the conditions of shielding and low texture in a complex scene is effectively improved by combining language prompt and registration and rendering fine optimization guided by two-stage learning.

The invention discloses a dynamic three-dimensional scene representation method based on Gaussian splashing, and belongs to the field of machine vision. A first time feature constructed through a first neural network based on the time index is fused with the Gaussian feature of the original Gaussian point cloud, and then decoding is performed to obtain various parameters of the original Gaussian point cloud; and fusing a second time feature obtained through second neural network coding based on the time index with the feature obtained by the micro renderer, so that time domain information is introduced in the image reconstruction process to assist in constructing the incidence relation of adjacent video frames, and the time domain expression capability of the model is further enhanced. According to the method, the convolutional neural network is combined with Gaussian splash, the modeling capability of a model for a dynamic scene can be effectively enhanced by fusing time domain features, and efficient expression of a dynamic three-dimensional scene is realized; time domain redundancy is eliminated by modeling a time domain incidence relation, and efficient expression of a dynamic scene is achieved.

The invention relates to the technical field of computer vision, and discloses a sparse view-based three-dimensional image reconstruction method and device and computer equipment, and the method comprises the steps: obtaining a multi-view image of a target human body wearing clothes; fitting a three-dimensional human body template of the target human body; extracting a multi-view clothing image; according to the multi-view garment image and the three-dimensional human body template, generating an initial Gaussian point cloud of the garment; constructing a boundary range for the initial Gaussian point cloud, and removing free Gaussian points outside the boundary range; constructing a loss function of two-dimensional Gaussian sputtering, performing multiple optimization iteration training on the initial Gaussian point cloud, and continuously removing free Gaussian points outside the boundary range in the optimization iteration training process to obtain a reconstructed garment model; generating a three-dimensional human body model with clothes according to the three-dimensional human body template and the reconstructed clothes model; according to the method, the quality and availability of three-dimensional human body model reconstruction are improved.

The invention discloses an object level motion blur three-dimensional scene synthesis method and an object level motion blur three-dimensional scene synthesis device based on three-dimensional Gaussian splash (3D Gaussian splash, 3DGS). According to the method, a multi-view image and corresponding camera parameters are used as input, and three-dimensional Gaussian point scene representation including spatial position, scale, rotation, opacity and appearance characteristics is constructed; generating a binary mask of a moving foreground object and a static background by using an image segmentation model; on the basis, camera exposure time is dispersed into a plurality of time steps, space pose parameters changing along with time are introduced only for three-dimensional Gaussian points corresponding to the foreground object, instantaneous three-dimensional scene representation under each time step is obtained, integration or weighted averaging is carried out on rendering results of the plurality of time steps, and the real-time three-dimensional scene representation of the foreground object is obtained. Generating a motion blur rendering result with real physical significance; meanwhile, a mask-guided foreground-background separation rendering strategy and a partition loss function are adopted, clear rendering supervision is applied to a static background area, fuzzy rendering supervision is applied to a moving foreground area, and joint optimization of three-dimensional Gaussian scene parameters is achieved; in the reasoning stage, the clear background and the fuzzy foreground are fused and output according to the mask, and a three-dimensional scene reconstruction result with the clear background and the reasonable motion fuzzy effect of the foreground is obtained. According to the method, the exposure integral process and the object movement track are explicitly modeled in the three-dimensional geometric space, and object-level controllable fuzzy three-dimensional scene generation is realized.

The invention discloses a text-to-three-dimensional Gaussian shape generation method and device, equipment and a medium, and the method comprises the steps: obtaining an initialized three-dimensional Gaussian model through a pre-trained three-dimensional generator according to the text input for generating a target task; performing multi-view projection to obtain a key view image and a group of auxiliary view images; performing feature extraction on the auxiliary view image, and fusing the extracted features with the features of the key view image through an attention mechanism; performing multi-view joint geometric optimization on the initialized three-dimensional Gaussian model through fractional distillation loss by using the fused features and text input injection diffusion model to obtain a three-dimensional Gaussian model subjected to geometric optimization; and a layered Gaussian constraint mechanism is implemented, reinforced constraint is applied to Gaussian points generated earlier, higher degree of freedom is given to Gaussian points newly added in the later period, and a final three-dimensional Gaussian shape is obtained. According to the method, the geometric semantic consistency and the overall fidelity and fineness of the generated result under different visual angles are improved.

The invention discloses a head reconstruction method based on Gaussian sputtering, and belongs to the field of computer vision. Performing alignment and clone splitting on the initial point cloud model by using the supervision image to obtain a head accurate point cloud of the supervision image, and taking the head accurate point cloud position as a Gaussian point cloud position; projecting the Gaussian point cloud to an image coordinate system, and performing feature extraction by taking the supervised image as convolutional neural network input to obtain a convolutional feature map; sampling the head precise point cloud and the convolution feature map of the image coordinate system to obtain a feature vector, and decoding the feature vector to obtain a Gaussian point cloud parameter so as to obtain a three-dimensional head Gaussian model; a head image of any viewpoint is quickly rendered through the three-dimensional head Gaussian model, pixel-by-pixel loss calculation is performed on the rendered image and a real image, and the three-dimensional head Gaussian model is optimized. According to the method, under the condition of sparse input, the convolutional neural network and Gaussian sputtering are fitted, and the obtained high-fidelity three-dimensional head Gaussian model can be rendered quickly in real time and has generalization.

The invention provides a 3D GS anti-aliasing rendering method fused with adaptive expansion, which comprises the following steps: under a 3D GS framework, before projecting a 3D Gaussian to a screen space, firstly applying a 3D Gaussian low-pass filter to the 3D Gaussian to suppress high-frequency components exceeding a Nyquist frequency and reduce the probability of occurrence of aliasing artifacts; after a three-dimensional Gaussian is projected to a screen space, a resolution self-adaptive expansion coefficient adjusting mechanism is used, and the physical size of a Gaussian point can be automatically adjusted according to the image resolution. And finally, in combination with a super-sampling technology, it is ensured that the colors of the Gaussian primitives can reasonably influence the pixels. According to the multi-guarantee 3DGS anti-aliasing rendering method, the aliasing problem caused by overlarge coverage of Gaussian points is prevented under the low resolution, and detail loss and edge erosion are avoided under the high resolution.

The invention provides a modeling method and device for a photovoltaic power station, electronic equipment and a storage medium, and belongs to the technical field of computer vision, and the method comprises the steps: obtaining remote sensing images of the photovoltaic power station at a plurality of visual angles; generating sparse point clouds based on the remote sensing images of the multiple visual angles; three-dimensional reconstruction is conducted on the sparse point cloud through an improved Gaussian splashing processing mode, a three-dimensional model of the photovoltaic power station is generated, and the improved Gaussian splashing processing mode comprises the steps that on the basis of a self-adaptive Gaussian point control strategy, through self-adaptive adjustment of Gaussian points, geometric and visual features of the photovoltaic power station can be captured more accurately, and the three-dimensional model of the photovoltaic power station is generated. The precision of the three-dimensional model of the photovoltaic power station is improved, the complexity and calculation cost of the model are effectively controlled, unnecessary calculation resource consumption is reduced, and the modeling efficiency of the photovoltaic power station is improved, so that the modeling quality and efficiency of the photovoltaic power station are improved.

The invention belongs to the field of computer vision and artificial intelligence, and provides an X-ray three-dimensional reconstruction method and system based on geometric prior and perspective alignment, and the method comprises the steps: carrying out the projection of CT (Computed Tomography) volume data, generating a multi-angle original X-ray projection image, and carrying out the preprocessing of the multi-angle original X-ray projection image, and obtaining a multi-angle X-ray projection image; performing first initialization on the multi-angle X-ray projection image to obtain an initialized Gaussian point cloud; based on the initialized Gaussian point cloud, performing iterative training of a first set number of times by using a radiation Gaussian splashing model of perspective alignment, and optimizing Gaussian attributes in combination with a loss function after each time of training to obtain a three-dimensional Gaussian point cloud after iteration of the first set number of times; performing second initialization on the three-dimensional Gaussian point cloud iterated for the first set number of times to obtain a re-initialized Gaussian point cloud; and inputting the re-initialized Gaussian point cloud into a perspective alignment radiation Gaussian splash model for iterative training, and iterating for a second set number of times to obtain a three-dimensional reconstruction result.

The invention discloses a scene completion method based on Gaussian sputtering, and relates to the technical field of scene reconstruction. Obtaining an original video of a target scene collected by a camera device; performing Gaussian sputtering conversion on pixel points of each frame of image in the original video to obtain Gaussian distribution representation; the Gaussian distribution representation comprises spatial position and color information of a plurality of three-dimensional Gaussian points; inputting the spatial positions of all the three-dimensional Gaussian points into the improved point cloud completion model to obtain a complete Gaussian point cloud of the target scene; the improved point cloud completion model is constructed by introducing features extracted by a contrast language image pre-training CLIP model and a self-distillation visual converter DINO model as supervision information into a point cloud completion network PCN model; and according to the Gaussian distribution representation, performing color rendering on the complete Gaussian point cloud to obtain optimized Gaussian point cloud distribution. According to the method, a scene with a relatively vivid effect can be generated.

The invention discloses a digital human reconstruction method and system based on grid and Gaussian point mixed representation, and the method comprises the steps: carrying out the analysis and processing of a human body image in a video data set, and obtaining a human body grid and posture parameters; subdividing the human body grid, binding a vertex of the refined grid with a three-dimensional Gaussian point, and initializing a standard space Gaussian position; performing feature coding on the Gaussian position, and learning geometric attributes and color attributes of Gaussian points in combination with the feature codes and the attitude parameters to obtain a complete Gaussian attribute set; and according to the complete Gaussian attribute set, generating a digital human image by adopting a linear hybrid skin method in combination with optimization constraints. According to the invention, stable reconstruction and high-quality rendering of different character images under different postures can be realized.

The invention discloses a 3D Gaussian splash rendering optimization method based on a Unity engine in VR, and relates to the technical field of computer graphics. PLY point cloud files are converted into SOG high-compression formats containing multi-level LOD information, after low-transparency Gaussian points are removed, Morton coding sorting is carried out on the center coordinates of the Gaussian points, and then the 3D Gaussian splash rendering optimization method based on the Unity engine in VR is realized. The method comprises the following steps: constructing an octree spatial index containing a spatial bounding box and multiple LOD data, removing Gaussian points in a grading manner according to a camera distance, selecting an LOD hierarchy, carrying out deep barrel sorting on visible Gaussian points, executing drawing by taking a barrel as a unit, and combining near-to-far sorting, an improved mixed equation and screen space edge removal and amplification operation to obtain a target image. According to the method, through multi-dimensional optimization such as loading, space elimination and rendering, the loading efficiency of the VR equipment is improved, the rendering burden is reduced, Tile-Based hardware is adapted, and smooth operation of 3D Gaussian splashing on the VR all-in-one machine is realized.

The invention discloses a post-restoration efficient three-dimensional modeling method for building colored drawing, which comprises the following steps of: 1, introducing a pre-restoration module, and restoring an acquired picture by adopting a pre-trained SD (Secure Digital) model; filtering a part of hue gauss with relatively high overlap ratio; step 3, respectively obtaining Gaussian point clouds of the repaired part and the original scene by using the step 2; and after the optimized final point cloud representation is obtained, combining the repaired part with the Gaussian point cloud of the original scene, and modeling the scene into a Gaussian ellipsoid by imitating a 3DGS method, so as to repair the traditional building on the three-dimensional level. The method is based on a stable diffusion model (Stable Diffusion) and a 3DGS technology, aims to overcome the defects in building repair modeling in the prior art, and particularly can realize optimization on time and space overhead.

The invention discloses an integrated three-dimensional space modeling method based on Gaussian splashing and a three-dimensional point cloud. The method comprises the following steps: step 1, converting a scene multi-view image and depth data into the three-dimensional point cloud; 2, constructing a point sequence input set; step 3, inputting the point sequence input set into a PointMamba model; 4, performing Gaussian parameter initialization, performing scale expansion on a local sparse region, and performing direction constraint on a global key region; 5, obtaining a prediction depth map through a binocular parallax matching algorithm, calculating a rendering depth map of the initial Gaussian point set, and adjusting the optimization intensity; and step 6, executing an adaptive encryption operation, and outputting a three-dimensional space model. According to the method, high-precision three-dimensional reconstruction of a complex scene is realized, and the method is suitable for scenes needing high-density point cloud modeling and semantic understanding, such as building scanning, industrial detection and virtual reality.

The invention discloses a 3DGS-based point cloud geometric structure and rendering effect optimization method, and relates to the field of three-dimensional reconstruction. The method comprises the following steps: carrying out data acquisition on a target scene to obtain a scene image set, and preprocessing data in the scene image set; sparse reconstruction is carried out based on the preprocessed scene image set, and sparse point clouds of the scene are recovered; constructing a three-dimensional Gaussian point cloud based on the sparse point cloud obtained in the step 2, and flattening the three-dimensional Gaussian point cloud to obtain a two-dimensional Gaussian point cloud; carrying out two-dimensional Gaussian point cloud projection; creating a filter based on scale adaptation and acting the filter on the projected two-dimensional Gaussian point cloud to adaptively modify the distribution size of the two-dimensional Gaussian projection; rendering the two-dimensional Gaussian point cloud adjusted by the filter to obtain a rendered image; and according to a comparison result of the rendered image and the corresponding image in the preprocessed scene image set, carrying out back propagation to carry out two-dimensional Gaussian point cloud optimization. According to the invention, the scene reconstruction precision and the reality of the rendering effect can be improved.

The invention discloses an SLAM system optimization method and device based on a layered anchor point diagram structure and a storage medium, and the method comprises the steps: collecting and preprocessing sensor data, constructing a layered anchor point architecture composed of a structure anchor point, an appearance anchor point, a global anchor point and a dynamic anchor point, gaussian point distribution is restrained through anchor points, camera poses are mapped to appearance codes, absolute coordinates are associated, dynamic interference is processed, and efficient sharing is achieved through an anchor point diagram. According to the method, the dynamic environment adaptability is enhanced, the resource efficiency is optimized, the fidelity and robustness of the map are improved, the multi-machine cooperation bandwidth pressure is reduced, and the method is suitable for automatic driving, robot navigation and other scenes.

The invention provides a substation intelligent operation and maintenance method and cockpit system based on Gaussian model and Internet of Things data fusion, and the method comprises the steps: carrying out Gaussian point cloud modeling based on substation information, and obtaining an initial three-dimensional Gaussian point cloud model, associating the equipment attribute information with the initial three-dimensional Gaussian point cloud model based on equipment monomer identification to obtain a target three-dimensional Gaussian point cloud model; based on the parking area and the vehicle parameters of the transformer substation, simulating initial operation and maintenance parameters of the operation vehicle in the transformer substation, and based on the target three-dimensional Gaussian point cloud model and the initial operation and maintenance parameters, carrying out operation risk detection to obtain a risk detection result; optimizing the initial operation and maintenance parameters based on the risk detection result to obtain optimized operation and maintenance parameters, and generating an electronic operation and maintenance fence based on the optimized operation and maintenance parameters; and loading the optimized operation and maintenance parameters and the electronic operation and maintenance fence to the target three-dimensional Gaussian point cloud model for operation and maintenance safety early warning. The safety and efficiency of the maintenance operation of the transformer substation are improved.

The invention discloses a map construction method of a three-dimensional Gaussian SLAM (Simultaneous Localization and Mapping) algorithm based on depth information fusion, which comprises the following steps of: firstly, adopting a dynamic point cloud downsampling method based on depth information, so that the defect that structural information is easy to lose due to the fact that point cloud spatial distribution and geometric characteristics caused by the depth information are not considered in traditional random downsampling can be overcome; the sampling rate is dynamically adjusted through depth information, a key geometric structure is reserved while data redundancy is reduced, good geometric priori is provided for generation of Gaussian point clouds, and the generated Gaussian point clouds are more fit with the surface of an object; moreover, according to the method, the optimization efficiency in the map reconstruction process is improved through the perspective principle of depth information adaptive point size and simulation of human eye observation, and the geometric error of luminosity rendering of the reconstructed map is smaller; in addition, the depth information is optimized by utilizing multi-frame depth fusion, so that the geometric position generated by the Gaussian point cloud is more accurate, and the subsequent optimization time is reduced.

The invention discloses an augmented reality inspection method and device based on a building information model, and a medium. The method comprises the following steps: converting the building information model into a BIM conditional Gaussian point field and a semantic signed distance field, and collecting multi-source heterogeneous sensor data; constructing a joint optimization factor graph, calculating a current camera pose estimation value and a covariance matrix, and monitoring numerical values of a projection edge consistency residual error and an SDF distance residual error in real time; according to the uncertainty degree of pose estimation, the transparency and the shielding effect of augmented reality rendering content are dynamically adjusted, and when it is monitored that the alignment error of a local area exceeds a threshold value and the pose covariance is lower than the threshold value, local non-rigid deformation adjustment based on engineering tolerance constraint is triggered; generating a signature visual residual proof and carrying out digital signature and timestamp authentication; and mapping the signature visual residual proof to a pre-constructed hash tree to calculate a root hash value, and storing the root hash value and the digital signature to the block chain.