A 3D imaging system is proposed in which an object is successively illuminated in at least three directions and at least three images of the object are captured by one or more energy sensors. A set of images is produced computationally showing the object from multiple viewpoints, and illuminated in the at least three directions simultaneously. This set of images is used stereoscopically to form an initial 3D model of the object. Variations in the brightness of the object provides features useful in the stereoscopy. The initial model is refined using photometric data obtained from images in which the object is illuminated in the at least three directions successively.

Provided are a three-dimensional reconstruction method, apparatus and system of a dynamic scene, a server and a medium. The method includes: acquiring multiple continuous depth image sequences of the dynamic scene, where the multiple continuous depth image sequences are captured by an array of drones equipped with depth cameras; fusing the multiple continuous depth image sequences to establish a three-dimensional reconstruction model of the dynamic scene; obtaining target observation points of the array of drones through calculation according to the three-dimensional reconstruction model and current poses of the array of drones; and instructing the array of drones to move to the target observation points to capture, and updating the three-dimensional reconstruction model according to multiple continuous depth image sequences captured by the array of drones at the target observation points.

There are provided a method of vehicle inspection and a system thereof, the method comprising: obtaining a plurality of sets of images capturing a plurality of segments of surface of a vehicle at a plurality of time points; generating, for each time point, a 3D patch using a set of images capturing a corresponding segment at the time point, giving rise to a plurality of 3D patches; estimating 3D transformation of the plurality of 3D patches based on a relative movement between the imaging devices and the vehicle; and registering the plurality of 3D patches using the estimated 3D transformation thereby giving rise to a composite 3D point cloud of the vehicle. The composite 3D point cloud is usable for reconstructing a 3D mesh and/or 3D model of the vehicle where light reflection, comprised in at least some of the plurality of sets of images, is eliminated therefrom.

A system, comprising: a stereoscopic camera configured to acquire multiple pairs of images of a surface; a display; and a processor configured to: sequentially acquire multiple image pairs of a surface from the camera; incrementally construct a 3D model from the image pairs concurrently with the sequential image acquisition, by: for each currently acquired image pair, registering the currently acquired image pair to a location on the 3D model, and adding the currently acquired image pair to the 3D model when: a) the registration succeeds and b) a delta of the registered image pair exceeds a threshold; rendering the incremental construction of the 3D model on the display; and concurrently tracking the incremental construction by displaying a graphic indicator that simultaneously indicates: i) the registered location, ii) when the viewing distance is within a focal range, and iii) when the viewing distance is not within a focal range.

To generate 3D representation of a scene volume, the present invention combines the 3D skeleton approach and the shape from silhouette approach. The present invention efficiently works on complex scenes like sport events with multiple players in a stadium, with an ability to detect a wide number of interoperating 3D objects like multiple players.

An electronic device estimates a pose of one or more subjects in an environment based on estimating a correspondence between a data volume containing a data mesh based on a current frame captured by a depth camera and a reference volume containing a plurality of fused prior data frames based on spectral embedding and performing bidirectional non-rigid matching between the reference volume and the current data frame to refine the correspondence so as to support location-based functionality. The electronic device predicts correspondences between the data volume and the reference volume based on spectral embedding. The correspondences provide constraints that accelerate the convergence between the data volume and the reference volume. By tracking changes between the current data mesh frame and the reference volume, the electronic device avoids tracking failures that can occur when relying solely on a previous data mesh frame.

A method of processing light field images for separating a transmitted layer from a reflection layer. The method comprises capturing a plurality of views at a plurality of viewpoints with different polarization angles; obtaining an initial disparity estimation for a first view using SIFT-flow, and warping the first view to a reference view; optimizing an objective function comprising a transmitted layer and a secondary layer using an Augmented Lagrange Multiplier (ALM) with Alternating Direction Minimizing (ADM) strategy; updating the disparity estimation for the first view; repeating the steps of optimizing the objective function and updating the disparity estimation until the change in the objective function between two consecutive iterations is below a threshold; and separating the transmitted layer and the secondary layer using the disparity estimation for the first view.

The present teaching relates to method, system, medium, and implementation of determining depth information in autonomous driving. Stereo images are first obtained from multiple stereo pairs selected from at least two stereo pairs. The at least two stereo pairs have stereo cameras installed with the same baseline and in the same vertical plane. Left images from the multiple stereo pairs are fused to generate a fused left image and right images from the multiple stereo pairs are fused to generate a fused right image. Disparity is then estimated based on the fused left and right images and depth information can be computed based on the stereo images and the disparity.

A data capture system for object dimensioning includes: a motion sensor configured to generate a detection signal responsive to detecting an object at a capture position within a capture volume; a capture controller connected to the motion sensor and configured, responsive to receiving the detection signal, to generate and transmit a shutter command substantially simultaneously to each of a plurality of cameras that causes each camera to capture a respective image of a synchronous set of images of the capture volume; an image processing server connected to each of the plurality of cameras and configured to receive the synchronous set of images from the cameras, and to store the synchronous set of images in a common repository; the image processing server further configured to generate a point cloud representing the object based on the synchronous set of images, for use in determining dimensions of the object.

Embodiments described herein provide an apparatus comprising a processor to divide a first image projection into a plurality of regions, the plurality of regions comprising a plurality of points, determine an accuracy rating for the plurality of regions, and apply one of a first rendering technique to a first region in the plurality of regions when the accuracy rating for the first region in the plurality of regions fails to meet an accuracy threshold or a second rendering technique to the first region in the plurality of regions when the accuracy rating for the first region in the plurality of regions meets an accuracy threshold, and a memory communicatively coupled to the processor. Other embodiments may be described and claimed.