A method for performing region-of-interest (ROI)-based depth detection with aid of a pattern-adjustable projector and associated apparatus are provided. The method includes: utilizing a first camera to capture a first image, wherein the first image includes image contents indicating one or more objects; utilizing an image processing circuit to determine a ROI of the first image according to the image contents of the first image; utilizing the image processing circuit to perform projection region selection to determine a selected projection region corresponding to the ROI among multiple predetermined projection regions, wherein the selected projection region is selected from the multiple predetermined projection regions according to the ROI; utilizing the pattern-adjustable projector to project a predetermined pattern according to the selected projection region, for performing depth detection; utilizing a second camera to capture a second image; and performing the depth detection according to the second image to generate a depth map.

A method for performing region-of-interest (ROI)-based depth detection with aid of a pattern-adjustable projector and associated apparatus are provided. The method includes: utilizing a first camera to capture a first image, wherein the first image includes image contents indicating one or more objects; utilizing an image processing circuit to determine a ROI of the first image according to the image contents of the first image; utilizing the image processing circuit to perform projection region selection to determine a selected projection region corresponding to the ROI among multiple predetermined projection regions, wherein the selected projection region is selected from the multiple predetermined projection regions according to the ROI; utilizing the pattern-adjustable projector to project a predetermined pattern according to the selected projection region, for performing depth detection; utilizing a second camera to capture a second image; and performing the depth detection according to the second image to generate a depth map.

In some aspects, the techniques described herein relate to systems, methods, and computer readable media for data pre-processing for stereo-temporal image sequences to improve three-dimensional data reconstruction. In some aspects, the techniques described herein relate to systems, methods, and computer readable media for improved correspondence refinement for image areas affected by oversaturation. In some aspects, the techniques described herein relate to systems, methods, and computer readable media configured to fill missing correspondences to improve three-dimensional (3-D) reconstruction. The techniques include identifying image points without correspondences, using existing correspondences and/or other information to generate approximated correspondences, and cross-checking the approximated correspondences to determine whether the approximated correspondences should be used for the image processing.

In some aspects, the techniques described herein relate to systems, methods, and computer readable media for data pre-processing for stereo-temporal image sequences to improve three-dimensional data reconstruction. In some aspects, the techniques described herein relate to systems, methods, and computer readable media for improved correspondence refinement for image areas affected by oversaturation. In some aspects, the techniques described herein relate to systems, methods, and computer readable media configured to fill missing correspondences to improve three-dimensional (3-D) reconstruction. The techniques include identifying image points without correspondences, using existing correspondences and/or other information to generate approximated correspondences, and cross-checking the approximated correspondences to determine whether the approximated correspondences should be used for the image processing.

A user may select or interact with objects in a scene using gaze tracking and movement tracking. In some examples, the scene may comprise a virtual reality scene or a mixed reality scene. A user may move an input object in an environment and be facing in a direction towards the movement of the input object. A computing device may use sensors to obtain movement data corresponding to the movement of the input object, and gaze tracking data including to a location of eyes of the user. One or more modules of the computing device may use the movement data and gaze tracking data to determine a three-dimensional selection space in the scene. In some examples, objects included in the three-dimensional selection space may be selected or otherwise interacted with.

An imaging device includes a pixel region in which a plurality of pixels, each including a photoelectric converter, are arranged, including an effective pixel region, an optical black region covered with a light-shielding film, and a dummy pixel region arranged between the effective pixel region and the optical black region. The pixels arranged in at least the effective pixel region and the optical black region among the plurality of the pixels each include an optical waveguide arranged above the photoelectric converter. The pixels including the optical waveguides are arranged between the effective pixel region and the optical black region so as to be spaced apart from each other by at least a one-pixel pitch.

An imaging device includes a pixel region in which a plurality of pixels, each including a photoelectric converter, are arranged, including an effective pixel region, an optical black region covered with a light-shielding film, and a dummy pixel region arranged between the effective pixel region and the optical black region. The pixels arranged in at least the effective pixel region and the optical black region among the plurality of the pixels each include an optical waveguide arranged above the photoelectric converter. The pixels including the optical waveguides are arranged between the effective pixel region and the optical black region so as to be spaced apart from each other by at least a one-pixel pitch.

In one embodiment, a computing system may determine, determine an estimated distance of an eye of a user to a display plane of a display. The system may access correction maps corresponding to a number of reference distances to the display plane of the display. The system may select a first reference distance and a second reference distance based on the estimated distance. The system may generate a custom correction map for the user based on an interpolation of a first correction map corresponding to the first reference distance and a second correction map corresponding to the second reference distance. The system may adjust an image to be displayed on the display using the custom correction map. The custom correction map may correct non-uniformity of the display as viewed from the eye of the user. The system may display the image adjusted using the custom correction map on the display.

In one embodiment, a computing system may determine, determine an estimated distance of an eye of a user to a display plane of a display. The system may access correction maps corresponding to a number of reference distances to the display plane of the display. The system may select a first reference distance and a second reference distance based on the estimated distance. The system may generate a custom correction map for the user based on an interpolation of a first correction map corresponding to the first reference distance and a second correction map corresponding to the second reference distance. The system may adjust an image to be displayed on the display using the custom correction map. The custom correction map may correct non-uniformity of the display as viewed from the eye of the user. The system may display the image adjusted using the custom correction map on the display.

A novel disparity computation technique is presented which comprises multiple orthogonal disparity maps, generated from approximately orthogonal decomposition feature spaces, collaboratively generating a composite disparity map. Using an approximately orthogonal feature set extracted from such feature spaces produces an approximately orthogonal set of disparity maps that can be composited together to produce a final disparity map. Various methods for dimensioning scenes and are presented. One approach extracts the top and bottom vertices of a cuboid, along with the set of lines, whose intersections define such points. It then defines a unique box from these two intersections as well as the associated lines. Orthographic projection is then attempted, to recenter the box perspective. This is followed by the extraction of the three-dimensional information that is associated with the box, and finally, the dimensions of the box are computed. The same concepts can apply to hallways, rooms, and any other object.