369 results about "Stereo image" patented technology

The invention provides a navigation instruction generation method, device and system based on multi-modal environment understanding, and the method comprises the steps: obtaining multi-modal perception information comprising the three-dimensional image data and three-dimensional point cloud data of an environment where a current unmanned aerial vehicle is located, and the task text information of the unmanned aerial vehicle; and performing semantic fusion extraction on the multi-modal perception information and the task text information based on a visual language fusion model to obtain a multi-modal embedded representation, constructing a large language model Prompt based on a plurality of prior navigation templates and the multi-modal embedded representation, and inputting the large language model Prompt into the large language model. According to the method, the navigation instruction text output by the large language model is obtained, then the navigation instruction text is analyzed into the control instruction sequence which can be recognized and executed by the unmanned aerial vehicle, the control instruction sequence is issued to the unmanned aerial vehicle, and the accuracy, continuity and stability of navigation can be maintained in a complex, dynamic and GNSS limited environment.

The invention provides a robust stereo matching method fusing monocular semantic prior and multi-dimensional expert aggregation, relates to the technical field of computer vision and stereo matching, establishes a robust stereo matching framework fusing monocular semantic prior and multi-dimensional expert aggregation, and comprises a monocular branch and a binocular branch, inputting a stereo image into a robust stereo matching framework based on fusion of monocular semantic prior and multi-dimensional expert aggregation for matching calculation, wherein the matching calculation comprises the following steps: extracting semantic prior features of the input image by using a monocular branch; the method comprises the following steps: extracting a geometric enhancement feature map of a stereo image by using binocular branches, generating a matching cost body based on the geometric enhancement feature map, and carrying out refined iterative updating on the matching cost body to obtain a matched disparity map. According to the method, monocular and binocular depth estimation is cooperatively supported in a unified network architecture, and a parallel monocular depth prior path is utilized to actively guide and strengthen a core binocular matching process.

Embodiments of the present disclosure relate to surface estimation using stereo imaging and surface disparities. For example, a surface disparity field representing a surface in the environment (e.g., the ground) may be estimated from stereo image data and used for various downstream tasks. For example, the difference between a stereo disparity field and a ground disparity field may be used to detect objects, a representation of a navigable space may be generated by radially casting 2D rays in the ground disparity field, the ground disparity field may be used to compensate ego-motion for high dynamic attitude changes, and / or the ground disparity field may be lifted to 3D and used to fit a surface profile to points sampled from the lifted point cloud.

The invention discloses a low-canopy-density single-tree-scale young forest remote sensing extraction scheme fusing two-dimensional and three-dimensional features, and the scheme comprises the steps: generating a multi-view epipolar line image based on a two-linear-array three-dimensional image of GF-7 and RPC parameters; generating a pyramid image for the multi-view epipolar line image, and determining a disparity map of the two linear array stereoscopic images based on the pyramid image; calculating a point cloud based on the disparity map and carrying out grid processing on the point cloud to generate a DSM; laser height measurement data of a satellite is used for optimizing the DSM, the optimized DSM is obtained, an isolated forest algorithm is adopted for carrying out image analysis on the optimized DSM, and pixels containing young forests are extracted; fusing the panchromatic rearview image and the multispectral image of the satellite remote sensing image to generate a panchromatic-multispectral fused image; performing shadow extraction on the panchromatic-multispectral fusion image by adopting a shadow detection algorithm to obtain a target shadow region; the pixel containing the young forest is compared with the target shadow area, the target young forest extraction result is determined, and the accuracy of the young forest extraction result can be improved.

The invention discloses a vision and laser collaborative assembly positioning method and system, and belongs to the technical field of industrial automation control, and the method comprises the steps: obtaining stereo image data and positioning light spot data, generating multi-source input data, and carrying out the analysis and matching, calculating initial pose estimation, and carrying out the positioning of the three-dimensional image data and the positioning light spot data. Generating a visual positioning result, comparing the visual positioning result with theoretical position data, correcting deviation, generating cooperative positioning data, combining a standard operation process file, generating a visual guiding scheme containing a display instruction and a laser control instruction, synchronously executing the display instruction and the laser control instruction, realizing assembly guiding, and performing assembly. And meanwhile, the assembly process is monitored in real time to generate operation progress data, and the detailed degree of the visual guide scheme is adjusted in combination with historical operation data. According to the method, dynamic compensation and cooperative positioning are carried out by acquiring the three-dimensional image and the laser spot data, and a virtual-real combined guiding scheme is adjusted in real time, so that high-stability self-adaptive assembly process control can be realized.

The invention discloses a power distribution area load state monitoring method and system based on stereo portrait analysis, relates to the technical field of urban power grids, and effectively improves the positioning precision and response speed of a load fluctuation area by introducing a three-dimensional image reconstruction and dynamic partitioning mechanism; a graph neural network and space-time long-short-term memory network modeling method is combined, so that the trend perception capability of the load state evolution process is enhanced; further, multi-source fusion recognition of abnormal behaviors is realized through association matching of the image change track and the load feature sequence, and the monitoring accuracy and sensitivity are effectively improved; based on risk index assessment and a map visualization expression mode, an abnormal area positioning result is visual and transparent, and intelligent early warning and decision assistance are supported; the method has the advantages of high data fusion capability, high risk identification precision, high visual interactivity and the like, and the intelligent and practical level of load state monitoring of the power distribution area is remarkably improved.

The invention discloses a near-ground distance measurement method based on virtual large baseline four-eye vision, a medium and equipment, and the method comprises the steps: synchronously obtaining four images when a lifting appliance enters a near-ground operation range; based on the calibration relation between the cameras, the high-altitude images are respectively projected and transformed to the visual angles of the corresponding lifting appliance cameras and fused, two virtual camera images are generated, and therefore a virtual large base line far exceeding the physical distance is constructed; performing three-dimensional correction and cutting on the virtual image pair to obtain a row-aligned three-dimensional image pair; performing feature matching and triangulation on the virtual image pair to generate a sparse reference depth map; and inputting the stereo image pair and the sparse depth map into a pre-trained depth estimation neural network model together, outputting a dense depth map, and converting the dense depth map into a coordinate system taking the lifting appliance as an original point to obtain a vertical distance. According to the invention, the ultra-large baseline is virtually synthesized by using the existing camera, the remote distance measurement precision is improved, the cost is low, and the reliability is high.

The invention relates to the technical field of cigarette package anti-counterfeiting, in particular to a multilayer body with a 3D anti-counterfeiting effect, a preparation method of the multilayer body and an anti-counterfeiting packaging material. The multilayer body comprises a base material layer, a microlens array layer arranged on the front surface of the base material layer and a miniature image-text layer arranged on the rear surface of the base material layer, the base material layer comprises a protruding part and a plane part, the front surface of the base material layer comprises a convex surface and a front plane, and the rear surface of the base material layer comprises a concave surface and a rear plane; the micro lens array layer is arranged on the convex surface, and the miniature image-text layer is arranged on the concave surface. According to the invention, the microlens array layer forms the curved surface structure, so that the optical axis directions of the microlenses in different areas are naturally changed, a wider viewing angle is covered, and an observer can see a clear and distortionless three-dimensional image even if watching the three-dimensional image from a large lateral angle; and meanwhile, the light path change is more complicated, and a larger-amplitude and smoother dynamic effect is provided.

The invention provides a three-dimensional image modeling method and device based on plane data, and the method comprises the steps: carrying out the full-color / multispectral band fusion, bit depth adjustment, thin cloud removal, image enhancement, stripe removal and light and color uniformity of a satellite surveying and mapping image, and achieving the real color recovery of the image; performing splicing preprocessing on the plurality of small orthographic satellite surveying and mapping images after real color recovery, performing image registration on the to-be-registered image and a reference image, performing image geometric correction on the registered image, and performing image mosaic processing on the plurality of images after image geometric correction to obtain a single large-scene image; and three-dimensional image matching, building mask extraction, three-dimensional building modeling and three-dimensional building post-processing are carried out on a single large scene image to obtain a three-dimensional digital surface model, and ground three-dimensional real image modeling is completed. By applying the technical scheme of the invention, the technical problem that the existing two-dimensional surveying and mapping data is difficult to meet the increasing application requirements of three-dimensional scenes is solved.

The invention discloses a semantic segmentation method based on stereoscopic image super-resolution reconstruction guidance, and relates to the technical field of computer vision, and the method comprises the steps: firstly designing a semantic-super-resolution joint network with a double-branch structure; and simultaneously generating a high-resolution semantic segmentation map and a super-resolution reconstruction result of the left view through the semantic segmentation branch and the super-resolution branch. And secondly, in order to effectively utilize parallax information in the three-dimensional image to assist semantic segmentation, a parallax perception fusion module is designed, and semantic features are enhanced based on the parallax information, so that the segmentation accuracy is improved. Besides, in consideration of the fact that the super-resolution branch contains richer detail information and can guide the semantic segmentation task to learn better high-resolution representation, a semantic-detail interaction module is designed, the semantic-detail interaction module and the super-resolution branch can fully pay attention to complementary information clues of each other, and therefore performance improvement is further achieved.

The invention provides a multi-data combined construction site three-dimensional model construction method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the calling of a preset resolution satellite image, and obtaining a full-region stereo image pair data set; after radiation correction is carried out, a region surface model and a region elevation model are generated; grid-level modeling defect detection is carried out, and distributed blind area blocks are positioned; performing obstacle avoidance path fitting to generate an obstacle avoidance optimized flight path; the unmanned aerial vehicle is driven to execute multi-angle surrounding scanning, and distributed blind area dense point clouds are directionally collected; constructing a distributed blind area model; and carrying out microscopic superposition, and outputting a three-dimensional model of the construction site. The technical problems that a construction site modeling method in the prior art is limited in the aspects of coverage, modeling efficiency and data real-time performance, depends on a single data source, cannot provide a comprehensive and accurate three-dimensional model of a construction site, and affects the construction progress and decision are solved.

The invention provides a binocular stereo matching method and system, and the method comprises the steps: obtaining a left and right stereo image pair captured by a binocular camera, and carrying out the feature extraction and enhancement through a depth separable convolution basic network and an ECANet attention model; carrying out topological structure transformation by using a TopoAug feature enhancement strategy, constructing a topological consistency loss function and carrying out adaptive feature fusion; a large neighborhood search strategy and a hybrid node-destructor model are adopted to construct an initial cost body, and cost aggregation is carried out through capacity routing; and constructing an M uniform loss grid and a grid motion model to carry out parallax estimation, and obtaining a final parallax map through unsupervised consistency optimization. Through the technologies of lightweight network design, topology perception feature enhancement, hybrid node optimization cost body construction, unsupervised consistency optimization and the like, the calculation complexity is reduced while high precision is kept, and the method is particularly suitable for real-time monitoring scenes in resource-constrained environments such as substations and the like.

The invention discloses a multi-modal three-dimensional image quality evaluation method based on consistency-complementarity characteristics, and belongs to the technical field of three-dimensional image quality evaluation and brain-computer intelligence. The method comprises the following steps: S1, acquiring a plurality of groups of distorted stereo images and electroencephalogram signals corresponding to the distorted stereo images, preprocessing the distorted stereo images, and endowing a quality grade label to a sample according to a subjective experiment result; s2, inputting the preprocessed three-dimensional image and the electroencephalogram signal into a parallax perception image encoder and a multi-scale attention electroencephalogram encoder to obtain binocular image features and electroencephalogram spatial-temporal features; s3, inputting the binocular image features and the electroencephalogram spatial-temporal features into a complementary learning module, and extracting image complementary features, electroencephalogram complementary features and cross-modal consistency features; and S4, inputting the consistency characteristics into a consistency learning module for cross-modal alignment to obtain an optimized consistency characteristic representation, fusing the optimized consistency characteristic representation with the complementary characteristics, inputting the fused consistency characteristic representation into a classification module, and outputting a stereo image quality grade.

A crop harvester system includes an image sensor is positioned to capture a stereo image of crop material disposed in a region forward of a harvester implement. A radar system is positioned to receive a returned electromagnetic signal reflected from crop material in the region. A controller determines a volume of the crop material in the region from the stereo image, and determines a moisture content and a density of the crop material in the region from the returned electromagnetic signal. Based on the volume of the crop material, the moisture content of the crop material, and the density of the crop material in the region, the controller may then control one of a traction unit and the harvester implement while the harvester implement is cutting the crop material in the region to avoid plugging an auger of the harvester implement with the cut crop material.

The present application relates to the technical field of computers. Disclosed are an implementation method for an immersive conference, and a related electronic device. In the method, a 3D stereoscopic image of a second conference participant is constructed by using a second rendering model, and instead of an original video stream, only a relatively small amount of feature data needs to be transmitted, thereby effectively reducing the network bandwidth required by transmission; each conference participant can obtain the 3D stereoscopic image of the second conference participant at his / her own angle of view, thereby supporting a multi-person and multi-angle of view mode; and by means of the pre-constructed second rendering model, the 3D stereoscopic image of the second conference participant can be reconstructed in real time on the basis of the feature data, without the need for complex real-time rendering calculation, such that the overall rendering efficiency is improved, and even a terminal device with a relatively weak performance can also smoothly present a vivid 3D visual effect. The present application reduces the network bandwidth required by transmission, enables each conference participant to obtain an immersive experience at his / her own angle of view, and also improves the overall rendering efficiency.

A stereoscopic image display device includes a display device, a camera that detects the position of the user's viewpoint looking at this display device, and a rotation device that rotates the display device around a rotation axis passing through its display surface. This rotation device rotates the display device in synchronization with the movement of the user's viewpoint position so that the direction of the user's viewpoint as seen from the display surface does not change. A three-dimensional object defined in a world coordinate system that remains stationary relative to the real world is displayed on the display device, and when the user's viewpoint moves, an image of the object as seen from the direction of the user's viewpoint is displayed on the display device.

The invention relates to the technical field of planet remote sensing data processing and three-dimensional reconstruction, and discloses a Mars three-dimensional terrain reconstruction method based on feature enhancement and multi-view stereo matching, which comprises the following steps: firstly, obtaining and preprocessing Mars stereo image data; secondly, extracting texture features based on a gray-level co-occurrence matrix, and performing region segmentation by using local entropy to distinguish a high texture region from a low texture region; then, parameters such as the size of a matching block and a similarity threshold value are adaptively adjusted according to a segmentation result, and a multi-dimensional mixed feature descriptor is generated to perform homonymy point matching; a coupling threshold model of the solar incident angle and the terrain roughness is constructed, and parallax calculation of the shadow and the steep terrain is optimized; and finally, combining cross-scale parallax propagation and global bundle adjustment to reconstruct a three-dimensional terrain model. According to the method, the technical problems that the matching reliability of areas such as weak textures and shadows on the surface of Mars is insufficient and the model is discontinuous are solved, and the integrity, the self-adaptability and the global precision of the reconstruction model are improved.

The invention provides a curved surface laser engraving method and related equipment based on a camera module and active light spot projection, and the method comprises the following steps: controlling an active light spot projection module to project an active light spot on the surface of a workpiece placed on a processing table top of a laser engraving machine, and synchronously controlling the camera module to shoot, obtaining a three-dimensional image pair of the workpiece; performing parallax matching processing according to the three-dimensional image pair of the workpiece to obtain high-density point cloud data of the surface of the workpiece; constructing a workpiece curved surface three-dimensional model according to the high-density point cloud data; mapping the two-dimensional design pattern to a workpiece curved surface three-dimensional model to generate a laser engraving track containing curved surface height compensation; and controlling the laser engraving machine to perform curved surface laser engraving on the workpiece according to the laser engraving track. The curved surface laser engraving precision and the overall efficiency can be improved.

Embodiments of the present disclosure relate to surface estimation using stereo imaging and surface disparities. For example, a three-dimensional (3D) surface structure may be modeled as a disparity field, and a surface disparity field representing a surface in the environment (e.g., the ground) may be generated using a constrained nonlinear hierarchical optimization to process stereo image data and iteratively refine estimated surface disparity values based on weights that guide the optimization to expected surface values (e.g., ground, road). The resulting surface (e.g., ground) disparity field may be used for a variety of downstream tasks, such as obstacle detection, segmentation of a navigable space, ego-motion refinement, and / or generation of an estimated surface profile.

Plant analysis system includes a vehicle configured to traverse a field in which the plant is growing and an imaging device mechanically coupled to the vehicle. Imaging device is configured to generate stereo image data associated with the plant. A back-end computer system configured to store a machine learning algorithm that, when executed by a processor, cause the back-end computer system to receive the stereo image data from the imaging device, autonomously detect an object of interest associated with the plant based on the received stereo image data, characterize the detected object of interest, and estimate a crop yield based on the characterization of the detected object of interest.

Examples relate to a system, to a method and to a computer program for an optical imaging system, such as a microscope, and to an optical imaging system comprising such a system. The system is configured to obtain stereoscopic image data of a scene from a stereoscopic imaging device of the optical imaging system. The system is configured to determine depth information on at least a portion of the scene. The system is configured to determine a scaling factor of an information overlay to be overlaid over the stereoscopic image data based on the depth information. The system is configured to generate a stereoscopic composite view of the stereoscopic image data and the information overlay, with the information overlay being scaled according to the scaling factor. The system is configured to provide a display signal comprising the stereoscopic composite view to a stereoscopic display device.

The present application belongs to the technical field of image processing, and particularly relates to a real-time positioning method and system for mechanical arm operation, which comprises the following steps: calculating an initial matching cost volume of a stereo image pair; generating an adaptive aggregation path field according to a local structure tensor of the image, and determining an adaptive penalty term based on a structure strength; performing cost aggregation along the adaptive path field by using the adaptive penalty term to obtain an aggregated cost volume; determining a disparity map according to the aggregated cost volume and solving a three-dimensional pose of a target point to be drilled. The present application generates an aggregation path that conforms to the scene geometry and dynamically adjusts the smoothing constraint, thereby significantly improving the accuracy and robustness of three-dimensional positioning in a complex environment and providing reliable real-time pose guidance for high-precision operation of the mechanical arm.

The application discloses a device pose estimation method and system based on stereo images, and the method comprises the following steps: acquiring a synchronous stereo image pair and performing stereo correction; extracting feature information of left and right images and performing matching and screening to obtain effective matching point pairs; calculating spatial three-dimensional point information through triangulation, and removing invalid points to form an effective three-dimensional space point set; calculating a pose transformation matrix based on the effective three-dimensional space point set and the corresponding relationship of adjacent frame features; and updating the absolute pose based on a pose recursive model and outputting the result. The application also comprises robust processing steps such as pose quality evaluation, state classification management and adaptive update strategy selection. The application enhances the robustness of pose estimation in a complex environment, and improves the continuity and stability of pose estimation.

The application discloses a kind of orchard environment based on fusion GCNv2 network binocular vision SLAM method and system, comprising: using binocular camera to shoot the stereo image pair of orchard environment;Depth map is generated using stereo matching algorithm as label, construct and train GCNv2 network architecture including attention mechanism and multi-scale feature fusion;Input orchard environment image and extract key point, find left-right image corresponding relationship by descriptor matching, assign corresponding depth value to each matching key point using the depth map predicted by GCNv2 network;Filter feature points and update map with depth information;Loop detection and perform geometric consistency check, execute BA global optimization algorithm to correct errors in the entire trajectory and map;The application provides a kind of efficient, robust and suitable for orchard environment binocular vision SLAM method to improve the navigation ability and automation level of robot in complex orchard environment.

The embodiment of the application relates to the field of intelligent device information processing, and discloses an image display method and device, electronic equipment and a storage medium, wherein the image display method comprises the following steps: detecting a visual line drop point track formed by a visual line of a user projected on a refrigerator surface; selecting, based on the visual line drop point track, a storage article in the refrigerator corresponding to a current visual line drop point in the visual line drop point track; forming a stereoscopic image of the storage article; and displaying the stereoscopic image on a display screen on the refrigerator. The technical scheme disclosed in the application solves the problem of the low image definition of the storage article of the refrigerator, the high difficulty of article recognition, the low recognition and display accuracy, and the poor user experience of the refrigerator storage article management in the prior art, and can improve the image definition of the storage article, reduce the difficulty of article recognition, improve the recognition and display accuracy, and improve the user experience of the refrigerator storage article management.

A stereoscopic image display device is disclosed, comprising a structure in which a display panel includes a display area and a non-display area surrounding the display area and displays an image through the display area, a 3D lens is arranged on one side of the display panel, the display panel comprises a base substrate, at least one light-emitting element arranged on the base substrate, a black matrix arranged above the at least one light-emitting element and having at least one first aperture, at least one lens arranged in the at least one first aperture, and at least one color filter arranged such that it faces the at least one lens, and respective side sections of the at least one lens and the at least one color filter are arranged such that they correspond in the first aperture of the black matrix, and the device may be lighter and thinner.

The application relates to a binocular image key point matching method based on a hierarchical optimization strategy and a medium, and the method comprises the following steps: S1, acquiring multiple pairs of left-right stereo image pairs; S2, processing the left-right stereo image pairs by using a deep neural network feature extractor to obtain left-right feature map pairs with different resolutions; S3, calculating matching cost volumes of the left-right feature map pairs to construct a matching cost volume pyramid; S4, obtaining an initial key point matching pair based on a matching cost volume with the lowest resolution; S5, calculating the matching cost between corresponding image blocks of the key point matching pair in a matching cost volume with a lower resolution, and taking a pixel pair corresponding to the matching cost satisfying local extremum as a key point matching result of the lowest level; and S6, repeating step S5, and performing layer-by-layer optimization on the matching cost volume pyramid in a manner from low to high resolutions until a final key point matching result is obtained. Compared with the prior art, the application has the advantages of high precision and strong robustness.

The application discloses a parking identification method and system based on a multi-modal fusion identification curb machine, and belongs to the technical field of parking management. The method comprises the following steps: detecting a vehicle entering event through a built-in sensor of the curb machine and triggering perception data collection; when adjacent curb machines detect vehicles in similar time, a collaborative perception network is automatically established; each curb machine broadcasts the collected stereoscopic image and point cloud data combined with the geographical position code to the collaborative perception network and performs fusion, reconstructs a three-dimensional scene model, and determines the actual number of vehicle units through connected domain analysis; if it is a single vehicle, the best recognition unit is selected according to the three-dimensional contour and the spatial position relationship of the curb machine to perform a license plate recognition task. The application effectively solves the recognition problem of vehicle cross-position parking, avoids repeated billing or missed detection, and improves the accuracy of license plate recognition and the reliability of billing in complex scenes.

To provide a method and apparatus for generating a high depth-of-field image allowing a high depth-of-field image to be generated from a captured image without requiring an optical structure for multiple captures at different depths of field, and an apparatus for training a high depth-of-field image generation model therefor.SOLUTION: A high depth-of-field image generating apparatus according to the present invention includes a region segmentation unit which segments a region for a stereo image to generate region data, a depth estimating unit which estimates depths for the stereo image to generate depth data, and a high depth-of-field image generating unit which generates a high depth-of-field image from the stereo image, the region data and the depth data.SELECTED DRAWING: Figure 1

The invention discloses a multi-source visual feature fused sparse texture scene positioning method and system, and the method comprises the steps: firstly constructing a high-precision map, collecting a target scene image through a binocular camera, defining each pair of three-dimensional images as a map node, and recording the physical collection sequence of each node; performing multi-source feature extraction and fusion on each map node image to form a fusion feature set which keeps high robustness under various texture conditions; and for each matching point pair in the fusion feature set, obtaining a three-dimensional coordinate of the matching point pair in a camera coordinate system by using internal and external parameters of a binocular camera and a triangulation principle, and storing the three-dimensional coordinates of all fusion feature points of each node to form a scene structure layer of the node. And determining the pose of each map node in the global unified coordinate system, and integrating the poses of all the nodes to form a node track, thereby completing map construction, and the constructed map can be used for vehicle positioning. According to the method, the problems of incomplete map information, poor positioning robustness and low precision caused by difficult feature extraction and matching of a single vision positioning method in sparse texture and high repeatability scenes such as underground parking lots can be effectively solved.