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8406 results about "Point cloud" patented technology

A point cloud is a set of data points in space. Point clouds are generally produced by 3D scanners, which measure many points on the external surfaces of objects around them. As the output of 3D scanning processes, point clouds are used for many purposes, including to create 3D CAD models for manufactured parts, for metrology and quality inspection, and for a multitude of visualization, animation, rendering and mass customization applications.

3D imaging system

The present invention provides a system (method and apparatus) for creating photorealistic 3D models of environments and/or objects from a plurality of stereo images obtained from a mobile stereo camera and optional monocular cameras. The cameras may be handheld, mounted on a mobile platform, manipulator or a positioning device. The system automatically detects and tracks features in image sequences and self-references the stereo camera in 6 degrees of freedom by matching the features to a database to track the camera motion, while building the database simultaneously. A motion estimate may be also provided from external sensors and fused with the motion computed from the images. Individual stereo pairs are processed to compute dense 3D data representing the scene and are transformed, using the estimated camera motion, into a common reference and fused together. The resulting 3D data is represented as point clouds, surfaces, or volumes. The present invention also provides a system (method and apparatus) for enhancing 3D models of environments or objects by registering information from additional sensors to improve model fidelity or to augment it with supplementary information by using a light pattern projector. The present invention also provides a system (method and apparatus) for generating photo-realistic 3D models of underground environments such as tunnels, mines, voids and caves, including automatic registration of the 3D models with pre-existing underground maps.

Parallax optimization algorithm-based binocular stereo vision automatic measurement method

InactiveCN103868460AAccurate and automatic acquisitionComplete 3D point cloud informationImage analysisUsing optical meansBinocular stereoNon targeted
The invention discloses a parallax optimization algorithm-based binocular stereo vision automatic measurement method. The method comprises the steps of 1, obtaining a corrected binocular view; 2, matching by using a stereo matching algorithm and taking a left view as a base map to obtain a preliminary disparity map; 3, for the corrected left view, enabling a target object area to be a colorized master map and other non-target areas to be wholly black; 4, acquiring a complete disparity map of the target object area according to the target object area; 5, for the complete disparity map, obtaining a three-dimensional point cloud according to a projection model; 6, performing coordinate reprojection on the three-dimensional point cloud to compound a coordinate related pixel map; 7, using a morphology method to automatically measure the length and width of a target object. By adopting the method, a binocular measuring operation process is simplified, the influence of specular reflection, foreshortening, perspective distortion, low textures and repeated textures on a smooth surface is reduced, automatic and intelligent measuring is realized, the application range of binocular measuring is widened, and technical support is provided for subsequent robot binocular vision.

Three-dimensional enhancing realizing method for multi-viewpoint free stereo display

The invention discloses a three-dimensional enhancing realizing method for multi-viewpoint free stereo display, which comprises the following steps: 1) stereoscopically shooting a natural scene by using a binocular camera; 2) extracting and matching a characteristic point of an image of a main camera, generating a three-dimensional point cloud picture of the natural scene in real time, and calculating a camera parameter; 3) calculating a depth image corresponding to the image of the main camera, drawing a virtual viewpoint image and a depth image thereof, and performing hollow repairing; 4) utilizing three-dimensional making software to draw a three-dimensional virtual model and utilizing a false-true fusing module to realize the false-true fusing of the multi-viewpoint image; 5) suitably combining multiple paths of false-true fused images; and 6) providing multi-viewpoint stereo display by a 3D display device. According to the method provided by the invention, the binocular camera is used for stereoscopically shooting and the characteristic extracting and matching technique with better instantaneity is adopted, so that no mark is required in the natural scene; the false-true fusing module is used for realizing the illumination consistency and seamless fusing of the false-true scenes; and the multi-user multi-angle naked-eye multi-viewpoint stereo display effect is supplied by the 3D display device.

Improved method of RGB-D-based SLAM algorithm

Disclosed in the invention is an improved method of a RGB-D-based simultaneously localization and mapping (SLAM) algorithm. The method comprises two parts: a front-end part and a rear-end part. The front-end part is as follows: feature detection and descriptor extraction, feature matching, motion conversion estimation, and motion conversion optimization. And the rear-end part is as follows: a 6-D motion conversion relation initialization pose graph obtained by the front-end part is used for carrying out closed-loop detection to add a closed-loop constraint condition; a non-linear error function optimization method is used for carrying out pose graph optimization to obtain a global optimal camera pose and a camera motion track; and three-dimensional environment reconstruction is carried out. According to the invention, the feature detection and descriptor extraction are carried out by using an ORB method and feature points with illegal depth information are filtered; bidirectional feature matching is carried out by using a FLANN-based KNN method and a matching result is optimized by using homography matrix conversion; a precise inliners matching point pair is obtained by using an improved RANSAC motion conversion estimation method; and the speed and precision of point cloud registration are improved by using a GICP-based motion conversion optimization method.

Urban environment composition method for unmanned vehicles

The invention discloses an urban environment composition method for unmanned vehicles. Under the condition of being independent of external positioning sensors such as speedometers, GPS and inertial navigators, the trajectory tracking and environment map building of an unmanned vehicle are completed by just using few particles for 3D laser-point cloud data returned by a vehicle-mounted laser radar, thereby providing basis for the autonomous running of the unmanned ground vehicle in an unknown environment; and according to the invention, an ICP algorithm is applied to adjacent two frames of data so as to obtain a coarse estimation on the real position and posture of a vehicle, and then redundance is performed near the coarse estimation based on gaussian distribution. Although the coarse estimation is not the real position and posture of the vehicle, the coarse estimation is a high-probability area of the real position and posture of the vehicle, so that an effect of relatively accurate positioning and composition is achieved by using a small amount of particles in the process of subsequent redundance, thereby avoiding the fitting of a vehicle trajectory by using a large amount of particles in a traditional method, improving the efficiency of the algorithm, and effectively restraining a phenomenon of particle degeneracy caused by bad particle estimation.
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