15 results about "Stereo reconstruction" patented technology

The invention discloses a photovoltaic module construction quality identification method based on computer vision and three-dimensional modeling. The method comprises the following steps: S1, collecting an original image sequence of a photovoltaic construction site and carrying out image preprocessing; s2, outputting a semantic tag graph and a two-dimensional space boundary graph through an improved BiSeNet network; s3, using a mask constraint SIFT algorithm to extract cross-view key point features, generating a dense three-dimensional point cloud according to multi-view stereo reconstruction, and outputting a structure three-dimensional geometric model; s4, mapping the semantic tag graph and the two-dimensional space boundary to the structure three-dimensional geometric model to obtain a semantic three-dimensional model; s5, performing spatial registration on the semantic three-dimensional model and the reference model, and calculating spatial error parameters of the photovoltaic module; s6, performing compliance discrimination on the spatial error parameters, and outputting a quality discrimination result; and S7, generating a construction quality evaluation report. According to the invention, automatic identification and accurate evaluation of the photovoltaic construction quality are realized, and the detection efficiency and the discrimination accuracy are improved.

The invention discloses a multi-view three-dimensional reconstruction method based on multi-scale structure perception fusion and confidence weighted optimization, and the method specifically comprises the following steps: 1, obtaining a multi-view data set, and dividing the data set into a training set and a test set; 2, constructing a depth estimation network, wherein the depth estimation network sequentially comprises a multi-scale feature extraction module, a structure perception and attention guidance feature fusion module, a confidence coefficient weighted feature enhancement module and a multi-stage depth estimation and refinement module; step 3, training the depth estimation network by using the training set in the step 1 to obtain a trained depth estimation model; and step 4, inputting the test set constructed in the step 1 into the model trained in the step 3 to obtain a predicted depth map of each reference view. The method solves the problems that an existing reconstruction method is insufficient in detail reduction in a weak texture region and fuzzy in boundary in a shielding and illumination variation region.

Computer-implemented procedure comprising the following steps: Performing a stereo alignment on a pair of images; Straightening (204) the image pair so that epipolar lines become either horizontal or vertical; Applying (206) a stereo alignment to the straightened image pair to establish an initial correspondence between pixels from the image pair; Generating a homography matrix transformation; Defining photo consistency between a template window in a base image of the image pair and a warp window of a translated image of the image pair as an implicit function of the homography matrix transformation; Improve the photo consistency of the warp window; Generating a translated pixel from a basic pixel, wherein the generation comprises repeatedly applying (208) the homography matrix transformation to the basic pixel until the absolute value of an increment step is less than a predetermined value; Triangulating (210) correspondence points to create a three-dimensional scene; and Providing the three-dimensional scene for display.

The application discloses a high-precision three-dimensional reconstruction method for a complex scene, which comprises the following steps: obtaining the position and orientation of a camera from multi-view images through polarization camera pose estimation; obtaining a scene surface normal vector through an MLP network; correcting an ambiguous polarization normal vector calculated by using a polarization image by using the scene surface normal vector, thereby obtaining a prior normal vector; inputting the prior normal vector into a pre-trained improved NeuS network to provide additional constraints, so as to alleviate the geometric blur problem of a textureless area and a mirror reflection area; and compared with depth prior, the network based on the prior normal vector can generate smooth geometry, can avoid the depth scale blur problem between multiple views, and can obtain more accurate three-dimensional information. The application can also perform high-precision reconstruction on an object with occlusion and an object with a thin structure, and can also perform better geometric reconstruction on a scene with sudden depth changes.

The application discloses an artificial intelligence generated video authentication method based on multi-view consistency. The method comprises the following steps: constructing a large-scale real-world simulation video dataset, using the previous frame as a prompt to generate a future frame video with high consistency in semantics, color and physical laws, and improving data authenticity; designing a detection model based on multi-view consistency physical prior, extracting video features through real-time multi-view matching, combining a time sequence memory module to dynamically store and update inter-frame stereo reconstruction features, and using an attention mechanism to enhance long-time consistency analysis; using a multi-modal large model to generate unified prompts and label data, and improving the model generalization ability; scoring each frame feature through a scorer and globally averaging to output a video authenticity judgment result. The application uses multi-view consistency prior and memory enhancement mechanism to effectively capture the subtle defects in AI generated videos that violate the real physical laws, and significantly improves the detection robustness and accuracy of high-quality generated videos.

This invention discloses an intelligent magnetic resonance holographic imaging method and system. The system includes a data acquisition device, an image reconstruction device, and an image display device. The data acquisition device acquires undersampled magnetic resonance data in real time through undersampled scanning and sends it to the image reconstruction device. The image reconstruction device inputs the undersampled magnetic resonance data into a deep learning model and outputs a high-dimensional stereoscopic reconstructed image. The deep learning model takes the undersampled magnetic resonance data of the sample as input and the corresponding fully sampled magnetic resonance data as output, and is obtained through self-supervised training. The image display device renders the stereoscopic reconstructed image in stereo and displays it in stereoscopic form through holographic projection. This invention enables rapid magnetic resonance imaging and real-time stereoscopic magnetic resonance image display, thereby better assisting doctors in surgical interventions and treatments.

The application discloses a multi-view reconstruction method and system based on geometric perception and attention fusion, and the reconstruction method designs a multi-scale feature enhancement network and a geometric perception feature fusion network; the multi-scale feature enhancement network extracts a multi-scale feature map and inputs the multi-scale feature map into a multi-stage depth estimation module to generate an initial depth map; the initial depth map is input into the geometric perception feature fusion network to generate a geometric perception enhanced feature map; the geometric perception enhanced feature map is input into the multi-stage depth estimation module to perform iterative optimization and output a refined depth map; and a joint loss function of a pixel-level cross-entropy loss function and a depth distribution similarity loss function is used to train a model constructed by the above network. The application effectively improves the expression capability of image features and the utilization efficiency of geometric information, focuses on solving the problem that the existing multi-view stereo reconstruction method has difficulty in depth estimation in a low-texture area and a structure repeated area, and significantly improves the accuracy and integrity of three-dimensional reconstruction.

The application discloses a kind of based on attention mechanism correction Kuroshio front stereo center line reconstruction method, including the following steps: (1) based on Soble algorithm obtains three-dimensional Kuroshio front area;(2) extract Kuroshio initial stereo center line;(3) using the characteristics of flow field and sea surface height anomaly field to remove outliers;(4) using attention mechanism to adjust correction field weight.The application first uses Soble algorithm to obtain the initial stereo center line of Kuroshio front, then uses the characteristics of flow field and sea surface height anomaly field to correct the stereo center line, and introduces attention mechanism to adjust the correction field weight, realizes the stereo reconstruction of Kuroshio front center line, and effectively describes the vertical swing characteristics of Kuroshio axis, provides an effective tool for the description of Kuroshio front three-dimensional characteristics.

The application discloses a method for stereoscopic reconstruction and visualization of nasal polyp tissue based on artificial intelligence recognition of continuous pathological sections, which comprises the following steps: strict continuous section of a reconstruction object; HE staining of the continuous sections; affine registration of the continuous pathological sections; one-by-one analysis of the registered continuous pathological sections; stacking according to the registration sequence, then processing, to obtain three-dimensional model data of the nasal polyp tissue structure; and visual rendering of the three-dimensional model data of the nasal polyp tissue structure, combined with a preliminary stereoscopic model, to obtain a final stereoscopic model of the nasal polyp tissue structure. The method can reconstruct and visualize the nasal polyp tissue, and can show the three-dimensional structure of the distribution characteristics of inflammatory cells and tissue structure of the nasal polyp in the spatial structure, so that the morphological structure content of the nasal polyp tissue is enriched, and morphological basis is provided for further exploring the occurrence and development mechanism of chronic rhinosinusitis with nasal polyps.

The application provides a three-dimensional space trajectory recognition method based on multi-view stereoscopic reconstruction, which comprises the following steps: using multiple cameras to shoot the same scene from different angles, determining the picture intersection part of the multiple cameras as the research scene; performing frame extraction and pedestrian detection on the video of each camera, recognizing the pedestrians and their position information in each frame of video, and assigning a uniform identity number to the same pedestrian; registering the video frame images of different cameras to obtain the pixel position of each pedestrian in each camera; solving the three-dimensional point coordinates of each pedestrian in the camera coordinate system by a multi-view triangulation method combined with the internal and external parameters of the camera; converting the three-dimensional point coordinates into the coordinates in the world coordinate system to obtain the world coordinate system coordinates of each numbered pedestrian point position in each frame; connecting the lines of each numbered pedestrian point position in each frame to form a trajectory according to the time information, and completing the reconstruction of the three-dimensional space trajectory of the pedestrian in the world coordinate system. The application can reduce the labor and calculation cost.

The invention discloses a monocular depth prior-based stereo reconstruction and obstacle detection method, which comprises the following steps of: after a binocular image is calibrated and corrected, fusing semantic features extracted by a lightweight neural network and depth prior information extracted by a monocular depth estimation network; constructing a multi-scale parallax cost body based on a deformable offset prediction module, and generating a geometric feature body and an initial parallax map of corresponding scales through three-dimensional convolution regularization; according to the semantic feature and the geometric feature, fusing a disparity map and a geometric feature corresponding to the multi-scale disparity cost body to obtain an initial disparity map and a geometric feature; and inputting the initial disparity map, the fused geometric features, the monocular depth priori and the context features into a convolution gating cycle unit, guiding the direction and the amplitude of disparity update to obtain a high-precision disparity result, converting the high-precision disparity result into a three-dimensional point cloud, and detecting an obstacle in real time by using Euclidean clustering and voxel expansion. The method has high reconstruction precision and robustness, and is suitable for stereo reconstruction and obstacle detection tasks.

The invention discloses a double-reflection non-vision-field scene three-dimensional reconstruction method based on a neural illumination field, and provides a neural illumination field model, three-dimensional coordinates and illumination attributes are used as input, and spatial continuous volume density and projection light intensity are parameterized through multi-layer perception, so that high-resolution three-dimensional representation is realized; the proposed intensity rendering equation further integrates dynamic lighting and static scene attributes to synthesize a shadow image, thereby eliminating the necessity of making error-prone binary segmentation. A large number of experiments show that high-fidelity three-dimensional reconstruction is realized in simulated and real scenes, and the resolution limit of 1cm is reached in a hidden space of 144 cubic meters. Even under the challenging conditions such as complex ambient light interference and low shadow contrast (the pixel difference is smaller than 10), the hidden object can still be recovered by the method, and strong environmental adaptability is shown.

The present disclosure relates to a camera calibration method, comprising: acquiring a plurality of captured images, the captured images being images of a calibration board captured by the camera from different distances and / or different angles, the calibration board containing a calibration image, the calibration image containing a plurality of discrete or continuous calibration points; acquiring calibration points corresponding to each pixel of the camera in the plurality of captured images; calculating ray information of the pixel in the physical world according to the coordinates of the calibration points in the physical world, and acquiring a set of ray information corresponding to each pixel of the camera to complete the calibration of the camera. To at least partially solve the lack of universality and accuracy of traditional calibration methods.

The application discloses an appearance defect detection system and method based on rapid three-dimensional reconstruction, and belongs to the technical field of optical three-dimensional reconstruction defect detection. The method improves the traditional photometric stereo method by flexibly constructing a multi-direction light source illumination image acquisition and processing system, accurately positioning a target area, and calibrating and correcting light intensity, rapidly solves target surface three-dimensional information images by using prior information, and improves the contrast of the metal workpiece surface defect image; the normal vector graph and the depth graph of different channels are used to design an image enhancement algorithm based on multiple channels by using the direction sensitivity advantages of the normal vector graph and the depth graph, and finally, a multi-level YOLO detection model based on multiple views is proposed, the detection results of each view are fused and decided, so that high-precision detection of the metal workpiece surface is realized.