532 results about "Stereopsis" patented technology

The invention relates to a concrete working performance measurement method and system based on a multi-modal visual large model, and solves the problem that rapid detection of concrete working performance parameters is troublesome, and the method comprises the steps: based on the spatial semantic understanding capability of the multi-modal visual large model, combining a multi-view stereoscopic vision and structured light scanning technology, and calculating the working performance of concrete; reconstructing a three-dimensional geometric structure of the concrete slurry, extracting morphological characteristic parameters, and forming characteristic vectors; inputting the feature vectors into a pre-trained multi-task neural network, fusing the spatial-temporal features and combining a rheological algorithm to identify various working performance parameters; integrating identification results for at least three times by adopting integrated learning, and verifying parameters based on a fluid dynamics basic equation through a fluid simulation platform; and based on the verification result, generating a mix proportion optimization suggestion containing the material components. The method has the advantages that non-contact rapid measurement of concrete working performance parameters is achieved, precision and efficiency are improved, and mix proportion optimization suggestions are provided.

A control method and system for precise landing of an unmanned aerial vehicle. The method comprises the following steps: on the basis of a multi-sensor fusion technique, using a lidar and a stereo vision algorithm to collect environmental data and perform primary processing, and generating comprehensive environmental perception data (step S1); on the basis of the comprehensive environmental perception data, using a digital elevation model algorithm to re-construct a three-dimensional terrain of a landing area, and generating a three-dimensional terrain model (step S2); on the basis of the three-dimensional terrain model, using an A* search path-planning algorithm to perform risk assessment and plan a safe landing path, and generating an optimized landing path (step S3); on the basis of the optimized landing path, using an adaptive control algorithm to adjust a flight attitude in real time by means of a fuzzy logic controller, and generating flight parameter adjustment (step S4); on the basis of the flight parameter adjustment, using machine vision and a decision tree algorithm to execute autonomous obstacle avoidance and emergency response, and generating a safe landing execution scheme (step S5); and on the basis of the safe landing execution scheme, using an ultrasonic sensor and a ground feedback system to confirm and fine-adjust a landing point, and completing final landing confirmation (step S6).

The invention discloses an automatic loading method for a truck based on deep learning and stereoscopic vision. The automatic loading method comprises the following steps that S1, goods location pixel coordinates are obtained and converted into an AGV forklift coordinate system; s2, a driving track is calculated, a front station is generated, and the AGV forklift is guided to move; s3, collecting point cloud data, denoising, extracting features by adopting an improved Transform point cloud registration network, calculating three-dimensional feature points of goods locations and trays, and converting coordinates; s4, calculating a rotation adjustment amount and a displacement compensation parameter, optimizing a driving track and correcting a butt joint deviation; s5, adopting a self-adaptive local attention weighting strategy to match the target goods allocation with the tray slot, and correcting the pose; s6, the cargo stacking posture is adjusted, gravity center distribution is optimized, and the AGV forklift is controlled to complete cargo loading; and S7, exiting the loading area along the optimized track, recording data and optimizing a loading strategy. The goods allocation identification precision is improved, the loading path is optimized, manual intervention is reduced, the loading efficiency, stability and adaptability are improved, and the logistics automation level is enhanced.

The invention provides a self-adaptive teaching-free welding robot intelligent gas saving monitoring system, which relates to the technical field of industrial robots, and comprises a three-dimensional sensing module used for identifying the workpiece material category through a multispectral sensor integrated with a welding gun of a welding robot, meanwhile, a stereoscopic vision camera and a line structure light scanning camera are used for obtaining three-dimensional point cloud data of the welding seam in real time; the parameter adjusting module is used for calling a heat conductivity coefficient database based on the three-dimensional point cloud data, and correcting the initial flow of the welding current, the arc voltage and the protective gas in real time; and the matching control module is used for calculating the arc length variable quantity based on the arc voltage, controlling the opening degree of the gas flow valve through a dynamic regulator, realizing dynamic matching of the gas flow and the changed arc length, and generating gas dynamic matching control parameters. Through cooperative operation of self-adaptive attitude compensation and real-time gas consumption monitoring, teaching-free accurate operation in the welding process is achieved, and intelligent control over gas consumption is improved.

The invention discloses a robust autonomous navigation and obstacle avoidance method for an underwater detection robot, and relates to the technical field of underground space intelligent identification and navigation.The method comprises the steps that through the multi-sensor fusion technology, a three-dimensional point cloud data set and a depth image data set of an underground environment are synchronously collected through a sonar and a stereoscopic vision system; the method comprises the following steps: constructing original geometric morphology data of an underground space, providing basic information for subsequent data processing, constructing a dynamic updating mechanism according to an initial navigation path data set, monitoring underground environment change and structural state through real-time sensor data, and regularly updating risk level values. If the risk level change exceeds a preset range, triggering path recalculation to ensure adaptation to underground environment change; the robust autonomous navigation and obstacle avoidance method of the underwater detection robot has important application value in underground engineering fields such as mines and tunnels, and provides technical support for intelligent management and safe operation of underground space.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention belongs to the technical field of stereoscopic vision, and discloses a depth estimation method based on adaptive structured light. The method comprises the following steps: acquiring a camera visual angle RGB image in a scene that a projector is turned off, acquiring a camera visual angle coarse disparity map through a semi-global matching algorithm, generating a projector visual angle RGB image and a projector visual angle coarse disparity map through projection transformation, and inputting the images into an adaptive structured light generation module to generate an adaptive structured light projection pattern; the adaptive structured light projection pattern generates a camera visual angle structured light image through a physical perception differentiable imaging module; the parallax attention depth estimation module receives the camera visual angle RGB image and the camera visual angle structured light image and outputs a depth estimation result; and taking the depth error as a loss function, jointly training and optimizing the adaptive structured light generation module and the parallax attention depth estimation module, and performing depth estimation after optimization. The optimal structured light mode is dynamically generated according to the real-time information of the scene, and the problem that traditional fixed-mode structured light cannot adapt to scene changes is solved.

The invention relates to the technical field of weld joint alignment tolerance detection of special equipment, spaceflight, bridges, ships, wind power and the like, such as containers, pipelines, boilers, recreation facilities, hoisting machinery, passenger ropeways and the like. The invention discloses an automatic weld joint alignment tolerance detection method fusing point cloud and image segmentation, which can quickly and accurately measure the alignment tolerance of a weld joint by combining binocular stereoscopic vision, AI and point cloud analysis technologies, and make up for the defects of the traditional detection method in efficiency and accuracy. The method comprises the following specific steps: shooting through a binocular structured light camera to obtain a grey-scale image and a point cloud image which are aligned; segmenting a welding seam on the grey-scale map, generating a position area mask according to the segmented welding seam, and dividing the actual object into an upper welding seam area, a welding seam area and a lower welding seam area; and according to the segmented welding seam, a welding seam boundary mask is extracted, the position mask and the boundary mask are mapped to a point cloud picture, and regional point cloud data and boundaries are obtained.

The invention relates to the technical field of underwater concrete three-dimensional reconstruction, in particular to an underwater concrete three-dimensional reconstruction method and system based on linear structured light and stereoscopic vision fusion. The method comprises the steps of obtaining a depth map and a texture map of underwater concrete based on a line structured light module and a stereoscopic vision module, and obtaining a preliminary depth data sequence and a surface texture sequence; performing environment compensation and image enhancement on the depth data and the texture data to generate data after environment correction; based on the corrected data, performing registration and matching on the depth and texture data by using a multi-modal fusion algorithm to generate a fusion point cloud data model; motion compensation and deformation repair are carried out on the point cloud model, deformation and structure offset caused by water flow are corrected, and a repaired point cloud model is generated; the repaired point cloud is input into a three-dimensional model reconstruction engine, a high-precision underwater concrete three-dimensional model is generated, intelligent analysis is conducted on the surface of the model, and damage and structural integrity of the model are evaluated. The underwater concrete three-dimensional reconstruction method effectively improves the precision of underwater concrete three-dimensional reconstruction, solves the problem of model distortion in complex environments such as water flow and turbid water quality, and has efficient and accurate structure detection and maintenance functions.

The invention discloses a method and a system for measuring the distance between cross positions of carrier cables of a rail contact network based on machine vision, and relates to the technical field of measurement of the distance between the cross positions of the carrier cables of the rail contact network. A laser radar and an infrared sensor are used for collecting depth information and temperature distribution data, and a multi-view image and depth temperature data are obtained; collecting the multi-view image and the depth temperature data to a central processing unit through a high-speed data transmission channel to obtain an initial data set; preprocessing the initial data set to obtain a high-quality data set; based on a deep learning algorithm and the high-quality data set, constructing a prediction model of the carrier cable and the intersection point, inputting sample data in the high-quality data set, and outputting prediction results of the carrier cable and the intersection point; and based on prediction results of the carrier cables and the intersection points, three-dimensional model reconstruction is carried out on the intersection positions of the carrier cables in combination with stereoscopic vision and structured light technologies.

The invention relates to the technical field of foundation pit detection, and discloses a foundation pit displacement monitoring method and system based on machine vision and monitoring equipment. The method comprises the steps that a high-definition camera collects image data of a foundation pit area in real time, the collected image data are preprocessed, reference points in the image data are analyzed through an optical flow method, displacement of the reference points in a foundation pit is estimated, and three-dimensional position changes of the reference points are calculated through the stereoscopic vision technology and image matching. The system comprises an image acquisition module, an image preprocessing module, a displacement calculation module, a filtering optimization module, an anomaly detection and early warning module and a data storage module. The equipment comprises a high-definition monitoring camera, a data processing terminal, a storage unit, an alarm device and a communication module. According to the invention, through image acquisition of a multi-camera system and a stereoscopic vision technology, comprehensive monitoring of the displacement of the foundation pit is ensured, high-quality image data can be provided in various environments, and precise acquisition of three-dimensional depth information is realized in combination with stereoscopic vision.

The invention relates to the technical field of optical localization, in particular to an optical localization feedback control system in a minimally invasive surgery of bile adipoma. The system comprises the following steps: an image optimization module is used for scanning a full-band optical image in an operation cavity, performing adaptive contrast optimization and constructing a spectrum optimization image; the focus positioning module is used for performing accurate focus positioning on the spectrum optimization image and extracting a focus optical contour; the three-dimensional reconstruction module is used for performing structural feature point identification and three-dimensional reconstruction on the spectrum optimization image to construct a three-dimensional structure model; and the visual error compensation module is used for performing multi-angle optical ranging and stereoscopic visual error compensation on the optical contour of the focus to obtain a three-dimensional compensation positioning coordinate. According to the invention, through real-time accurate position positioning, the instrument response speed and the tissue identification precision are improved.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, and discloses an unmanned aerial vehicle autonomous obstacle avoidance method based on deep learning and binocular vision. According to the method, original data streams of a left view and a right view are acquired through a binocular vision acquisition terminal, and stereoscopic vision feature representation is extracted through parallel convolutional coding branches of a deep neural network model; inputting into a three-dimensional space reconstruction module to generate a dense depth map and an obstacle initial position coordinate set; the dynamic obstacle analysis engine calculates a dynamic threat evaluation index by combining real-time flight attitude parameters of the unmanned aerial vehicle, and the space-time trajectory prediction model calculates future moving path probability distribution of the obstacle according to the dynamic threat evaluation index; and fusing the distribution with preset navigation path planning data to generate a three-dimensional obstacle avoidance course correction vector, converting the three-dimensional obstacle avoidance course correction vector into a flight control instruction set, and downloading the flight control instruction set to an execution module. According to the method, the reliability and adaptability of autonomous obstacle avoidance of the unmanned aerial vehicle in a complex dynamic environment are improved, and a powerful guarantee is provided for safe navigation of the unmanned aerial vehicle.

The invention discloses a visual angle tracking grating stereo display system comprising a 3D camera grating display device used for obtaining viewer 3D information; the eyeball position tracking module is used for acquiring real-time spatial position coordinates of the eyes of the audience relative to the 3D camera; the pose calibration module is used for acquiring a spatial relationship between the 3D camera and the display; and the visual angle rendering and grating interlaced graph generation module is used for calculating the spatial position of the human eyes on the display screen and the mapping of each grating strip in a screen pixel coordinate system after acquiring the positions of the eyes and the relative poses of the display screen and the 3D camera, generating a grating stereoscopic display image suitable for the eyes, and sending the grating stereoscopic display image to a display to be output. According to the method and the device, the viewpoint self-adaptive three-dimensional display of the eyes of one or more audiences is realized, the calculation complexity is reduced, the three-dimensional vision consistency can be ensured, the picture which should be seen by the viewing angle can be accurately rendered, and the problem of blurring or 3D picture distortion is avoided.

The invention discloses a vision-driven multi-mode fusion lightweight semantic map construction method and system. The method comprises the following steps: synchronously acquiring a binocular image pair sequence, IMU data and GNSS data of a target area; based on the acquired multi-modal data, performing multi-sensor joint state estimation through a differential weighted fusion strategy, and outputting camera global pose and scene depth information; based on a current frame and a historical frame in the binocular image pair sequence, combining a camera global pose, extracting geometric prior auxiliary time sequence cross-frame semantic feature fusion through stereoscopic vision, and outputting a two-dimensional semantic segmentation result of the current frame; and back-projecting the two-dimensional semantic segmentation result into a lightweight global three-dimensional semantic map based on camera pose and scene depth information, and carrying out maintenance and updating through a voxelization statistical mechanism. Compared with a traditional dense point cloud map, the method has the light weight effect that the storage space is greatly reduced.

The invention discloses a power transmission tower deformation monitoring method and device and a computer program product, and the method comprises the steps: obtaining a multi-angle collection image of a random speckle pattern on the surface of a power transmission tower; constructing an image sequence based on the multi-angle acquired images, reconstructing a three-dimensional displacement field on the surface of the power transmission tower by using a stereoscopic vision reconstruction algorithm, and exporting a strain tensor field; performing time sequence processing on the strain tensor field under a plurality of time nodes through a strain evolution analysis model of a continuous time sequence, and extracting a strain change trend of each preset structure part; carrying out space consistency mapping and weighted interpolation processing according to the strain change trends of the plurality of preset structure parts, and calculating to obtain the overall structural dependent variable of the power transmission tower; and S5, in combination with historical strain data and the current overall structural dependent variable, identifying an abnormal deformation area of the power transmission tower by comparing the spatio-temporal evolution mode of the strain tensor field. The method has the effect of improving the accuracy of deformation monitoring of the power transmission tower.

The invention provides an injection molding part surface flaw detection method and device based on double-model collaboration, and the method comprises the steps: carrying out the local abnormal reflection feature analysis of a multi-view optical image collected on the surface of an injection molding part based on a first detection model, and obtaining a candidate flaw region; performing spatial positioning in the multi-view optical image based on the candidate flaw area to obtain a multi-view candidate image segment; performing surface geometric continuity analysis on the multi-view candidate image segments based on a second detection model to obtain a three-dimensional surface geometric consistency result; carrying out authenticity discrimination on the candidate flaw area based on a three-dimensional surface geometric consistency result to obtain a real flaw area; wherein the first detection model is used for capturing an optical response model of local abnormal reflection characteristics; the second detection model is a stereoscopic vision model for analyzing the geometric continuity of the multi-view lower surface. According to the invention, the false alarm rate of surface defect detection of the injection molded part under a complex surface condition is reduced.

The invention discloses a bar count visual identification automatic calculation system and method, the system comprises a stereo visual acquisition unit, a dynamic threshold adjustment unit, a geometric feature analysis unit, a data processing module and a man-machine interaction unit, and the method comprises the following steps: S01, image acquisition and preprocessing; s02, adjusting an environment self-adaptive threshold value; s03, bar geometric feature extraction; s04, the number of bars is calculated; s05, outputting a result, displaying the bar count obtained by calculation on an interface of a man-machine interaction unit, and storing a counting result and related data in a database at the same time; the beneficial effects of the invention are that a conventional visual counting system usually only depends on two-dimensional image information, recognition errors are easy to occur under the conditions of bar adhesion, surface reflection and the like, the scheme constructs a stereoscopic vision system through an industrial camera to obtain three-dimensional point cloud data, image textures are enhanced in combination with coding structured light projected by a structured light emitter, and the recognition accuracy is improved. And the spatial position and surface depth information can be obtained.

The invention relates to a charging pile multi-mode vehicle security monitoring method and system, and the method comprises the steps: collecting vehicle contour point cloud, environment parameters and voltage fluctuation data through a stereoscopic vision sensor, a temperature and humidity sensor and a current sensor, and generating a multi-mode fusion data set through time-space synchronization; comprehensive scores of vehicle deformation, environment sudden change and voltage abnormity are calculated based on a dynamic weight fusion model, low-risk, medium-risk and high-risk three-level safety judgment results are generated in combination with a quantile threshold interval, and the environment adaptability defect of a traditional fixed rule is eliminated through a dynamic weight and an adaptive threshold; triggering a hierarchical control instruction according to a judgment result, and synchronously constructing a credible evidence chain containing a timestamp, an operation log and an encryption check code to ensure that the security event is traceable and the data is tamper-proof; and finally, aggregating multi-node user feedback through a federated learning framework, updating a model weight and a threshold interval in combination with a differential privacy technology, and realizing dynamic optimization of a false alarm rate while protecting user privacy.

The invention provides a bucket wheel machine material taking method and device based on image processing, equipment and a medium, and relates to the technical field of engineering control, and the method comprises the steps: carrying out the stereoscopic vision construction of an image in material pile image information according to a deep learning network, and obtaining a material pile three-dimensional model; a dynamic environment model is constructed based on the material pile three-dimensional model and the material pile dynamic change parameters, track planning is conducted on the initial position of the bucket wheel machine through the reinforcement learning technology, and the walking track of the bucket wheel machine is obtained; according to the walking track of the bucket wheel machine, the segmentation information in the material pile image information and the layering height in the material pile three-dimensional model, motion state prediction is conducted, and the rotation track of the bucket wheel machine around a cantilever is obtained; construction is carried out according to the walking track of the bucket wheel and the rotation track of the bucket wheel machine around the cantilever, and a material taking scheme is obtained by combining evaluation and optimization of historical material taking data, material type characteristics and stacking stability. The problems that the material taking efficiency is low, part of stacked materials are omitted or the materials cannot be effectively taken at all are solved.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention discloses a multi-source information fusion-based tunneling equipment vision-assisted pose detection system and method, and relates to the technical field of exploration and engineering surveying, and the system comprises a binocular vision module, a strapdown inertial navigation module, a laser orientation instrument module and a data fusion processing module. The binocular vision module solves three-dimensional coordinates through stereoscopic vision; the strapdown inertial navigation module provides high-frequency dynamic attitude data; the laser orientation instrument modules are triangularly arranged to provide a global absolute position reference; and the data fusion processing module adopts an extended Kalman filtering dynamic fusion algorithm and combines dynamic weight adjustment to fuse multi-source data. The method comprises the steps of data acquisition, preprocessing and time-space synchronization, fusion calculation and pose information output. Through the multi-source collaborative fusion and dynamic environment adaptation technology, the detection precision and stability are improved, the automatic tracking and remote monitoring requirements of the tunneling equipment are met, and intelligent upgrading of coal mine tunneling is promoted.

The invention relates to the field of pipeline three-dimensional reconstruction and intelligent detection, in particular to a pipeline three-dimensional reconstruction and intelligent detection method based on panoramic stereoscopic vision. According to the technical scheme, a panoramic camera and a radar sensor are used for collecting panoramic images and depth data in a pipeline; carrying out denoising, distortion correction and illumination compensation processing on the collected panoramic image, and carrying out image optimization; stable feature points in the panoramic image are extracted, and feature point matching is carried out; a multi-view stereoscopic vision algorithm and a synchronous positioning and mapping technology are combined, and pose estimation and three-dimensional point cloud generation are carried out by using a panoramic image and depth data; optimizing the generated point cloud data, and reconstructing a smooth and continuous three-dimensional surface; and based on the processed three-dimensional data, intelligently detecting cracks and corrosion defects in the pipeline by using a convolutional neural network, and automatically generating defect types, positions and repair suggestions. The method is suitable for pipeline detection.

A long-baseline and long depth-range stereo vision system is provided that is suitable for use in non-rigid assemblies where relative motion between two or more cameras of the system does not degrade estimates of a depth map. The stereo vision system may include a processor that tracks camera parameters as a function of time to rectify images from the cameras even during fast and slow perturbations to camera positions. Factory calibration of the system is not needed, and manual calibration during regular operation is not needed, thus simplifying manufacturing of the system.

The invention discloses a safe driving map construction method based on binocular vision and IMU information fusion, and the method comprises the following steps: obtaining image data information through a binocular image collection unit, obtaining IMU data information through an inertial measurement unit, and carrying out the data preprocessing of the image data information and the IMU data information; performing joint initialization processing on a data preprocessing result based on binocular vision initialization and inertial measurement unit initialization; carrying out global pose nonlinear optimization processing on a joint initialization processing result based on a tight coupling optimization strategy of a sliding window; based on a BVAI-Fusion algorithm, generating a safe driving track map for a global pose nonlinear optimization processing result; initial pose estimation is improved through binocular vision and IMU multi-source heterogeneous information fusion, so that pose estimation precision and stereoscopic vision matching under multi-source heterogeneous fusion are improved, SLAM environment positioning accuracy is enhanced, and safety of driving map construction is guaranteed.

The invention discloses a three-dimensional wave liquid level visual intelligent reconstruction evaluation system, and belongs to the field of ocean engineering monitoring. The system comprises an image acquisition module, an image stereoscopic vision processing module, an attention-enhanced reconstruction neural network module, a camera attitude evaluation module, a visualization and output module and a hydrodynamic parameter analysis module. An image is collected through a fixed baseline binocular camera system, after preprocessing, a neural network fused with a multi-scale attention mechanism is utilized to reconstruct a three-dimensional wave structure, coordinate system conversion is achieved in combination with self-supervised attitude evaluation, and finally hydrodynamic parameters such as significant wave height and a three-dimensional velocity field are extracted and visualized. The system does not need explicit calibration and large-scale data, has high automation, real-time performance and strong environmental adaptability, can be deployed on various platforms, and significantly improves the precision and engineering applicability of non-contact wave observation.

Provided is an external environment recognition device capable of determining a place where a host vehicle can safely evacuate at the time of recognition of a specific vehicle by using a plurality of cameras in combination even in a vehicle not equipped with a distance sensor such as a radar, a LiDAR, an ultrasonic sensor, or an infrared sensor. Therefore, an external environment recognition device includes: a plurality of cameras installed so as to have a plurality of stereo vision regions in which at least a part of a visual field region overlaps around a host vehicle; a three-dimensional information generation unit that generates three-dimensional information by performing stereo matching processing in each of the plurality of stereo vision regions; a three-dimensional information accumulation unit that accumulates the three-dimensional information generated during traveling of the host vehicle in time series; and a three-dimensional information update unit that updates the three-dimensional information accumulated in the three-dimensional information accumulation unit using three-dimensional information newly generated by the three-dimensional information generation unit.

A decoding computing device receives a bit stream for compressed volumetric visual content, such as immersive media. The bit stream includes video encoded image frames comprising packed attribute patch images, depth maps for a 3D scene represented by the volumetric visual content, and a variable transparency layer indicating boundaries of objects at different depths within the 3D scene represented by the volumetric visual content. Instead of generating a mesh having a large number of vertices, the decoder generates a lower resolution mesh and applies the variable transparency layer, such that portions of triangles or polygons of the lower resolution mesh extending beyond the boundaries of the objects at the different depths of the 3D scene are made transparent and not shown as being stretched across large depth distances.

The invention discloses a binocular stereoscopic vision electric power operation safety distance measurement method and system, relates to the technical field of image processing, and solves the problem that target identification is difficult to carry out according to a binocular image; secondly, feature extraction is difficult to carry out according to a target recognition result, and feature matching is difficult to carry out; thirdly, a three-dimensional space model is difficult to reconstruct after a disparity map is difficult to construct according to a feature matching result; and finally, the technical problems of difficulty in analyzing and calculating the safety distance among the electric power operation personnel, the electrified equipment and the large-scale high-altitude operation vehicle and difficulty in comprehensively analyzing and evaluating the safety of the electric power operation site are solved. According to the method, feature extraction and feature matching are carried out after electric power operation personnel, live-line equipment and a large-scale high-altitude operation vehicle in a binocular image are identified; the safety distances among the electric power operating personnel, the live-line equipment and the large overhead working vehicle are measured by constructing a disparity map and reconstructing a three-dimensional space model.