An exemplary data precision preservation system divides a depth representation into a first section and a second section separate from the first section. The system determines data bits representing numbers that correspond to a lowest non-null depth value and a highest non-null depth value represented in the first section, and converts an original set of depth values represented in the first section to a compressed set of depth values normalized based on the lowest and highest non-null depth values represented in the first section. The system then generates a dataset that includes data representative of the compressed set of depth values and an inverse view-projection transform that is based on the lowest and highest non-null depth values represented in the first section and is configured to facilitate conversion of the compressed set of depth values back to the original set of depth values. Corresponding systems and methods are also disclosed.

Some embodiments relate to a machine-implemented method of packing volumetric image data executed by at least one processing device, the method comprising: determining a first block size; writing to memory a first block of image data from a first image, the first block having the first block size; determining a second block size; and writing to memory a second block of image data from a second image, the second block having the second block size; wherein the first image contains X by Y pixels of one of colour data and depth data, and the second image contains X by Y pixels of the other of colour and depth data; and wherein the first image is related to the second image. Embodiments also relate to methods of unpacking volumetric image data. Further embodiments relate to systems and computer-readable media storing or having access to code to execute the packing and unpacking methods.

Implementations are provided that relate, for example, to view tiling in video encoding and decoding. A particular method includes accessing a video picture that includes multiple pictures combined into a single picture (826), accessing information indicating how the multiple pictures in the accessed video picture are combined (806, 808, 822), decoding the video picture to provide a decoded representation of at least one of the multiple pictures (824, 826), and providing the accessed information and the decoded video picture as output (824, 826). Some other implementations format or process the information that indicates how multiple pictures included in a single video picture are combined into the single video picture, and format or process an encoded representation of the combined multiple pictures.

A user authentication system and method. A two-dimensional image of a scene is obtained and range information obtained from the scene is aligned with the two-dimensional image. One or more depth regions is identified and image segments corresponding to the one or more depth regions are selected within the two-dimensional image. Brightness operations are performed on one or more of the selected image segments to form a corrected image.

Three-dimensional (3D) depth imaging systems and methods are disclosed for automatic container door status recognition. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. A container recognition application (app) receives the 3D image data, and determines therefrom a container point cloud representative of the shipping container, and a 3D image door area data subset defining one or more door areas of one or more corresponding doors of the shipping container. The container recognition app determines, from the 3D image door area data subset, a status type, of each of the one or more corresponding doors, selected from one of: (a) a door opened status type, (b) a door closed status type, or (c) a door partially closed status type.

The usual coding order according to which the reference view is coded prior to the dependent view, and within each view, a depth map is coded subsequent to the respective picture, may be maintained and does lead to a sacrifice of efficiency in performing inter-view redundancy removal by, for example, predicting motion data of the current picture of the dependent view from motion data of the current picture of the reference view. Rather, a depth map estimate of the current picture of the dependent view is obtained by warping the depth map of the current picture of the reference view into the dependent view, thereby enabling various methods of inter-view redundancy reduction more efficiently by bridging the gap between the views. According to another aspect, the following discovery is exploited: the overhead associated with an enlarged list of motion predictor candidates for a block of a picture of a dependent view is comparatively low compared to a gain in motion vector prediction quality resulting from an adding of a motion vector candidate which is determined from an, in disparity-compensated sense, co-located block of a reference view.

Using the same image sensor to capture both a two-dimensional (2D) image of a three-dimensional (3D) object and 3D depth measurements for the object. A laser point-scans the surface of the object with light spots, which are detected by a pixel array in the image sensor to generate the 3D depth profile of the object using triangulation. Each row of pixels in the pixel array forms an epipolar line of the corresponding laser scan line. Timestamping provides a correspondence between the pixel location of a captured light spot and the respective scan angle of the laser to remove any ambiguity in triangulation. An Analog-to-Digital Converter (ADC) in the image sensor generates a multi-bit output in the 2D mode and a binary output in the 3D mode to generate timestamps. Strong ambient light is rejected by switching the image sensor to a 3D logarithmic mode from a 3D linear mode.

A gain in multi-view coding is achieved as follows: the residual signal involved with coding a dependent view of the multi-view signal is predicted from a reference residual signal of the current picture of the reference view using block-granular disparity-compensated prediction, i.e. using disparity compensated prediction with a disparity defined at, and varying with, block granularity so that each block of the current picture of the dependent view has its own disparity displacement such as its own disparity vector, associated therewith. In other words, a remaining similarity between the residual signal involved with predictively coding the reference view is used in order to predict the residual signal involved with predictively coding the dependent view.

Using the same image sensor to capture both a two-dimensional (2D) image of a three-dimensional (3D) object and 3D depth measurements for the object. A laser point-scans the surface of the object with light spots, which are detected by a pixel array in the image sensor to generate the 3D depth profile of the object using triangulation. Each row of pixels in the pixel array forms an epipolar line of the corresponding laser scan line. Timestamping provides a correspondence between the pixel location of a captured light spot and the respective scan angle of the laser to remove any ambiguity in triangulation. An Analog-to-Digital Converter (ADC) in the image sensor generates a multi-bit output in the 2D mode and a binary output in the 3D mode to generate timestamps. Strong ambient light is rejected by switching the image sensor to a 3D logarithmic mode from a 3D linear mode.

Using the same image sensor to capture both a two-dimensional (2D) image of a three-dimensional (3D) object and 3D depth measurements for the object. A laser point-scans the surface of the object with light spots, which are detected by a pixel array in the image sensor to generate the 3D depth profile of the object using triangulation. Each row of pixels in the pixel array forms an epipolar line of the corresponding laser scan line. Timestamping provides a correspondence between the pixel location of a captured light spot and the respective scan angle of the laser to remove any ambiguity in triangulation. An Analog-to-Digital Converter (ADC) in the image sensor generates a multi-bit output in the 2D mode and a binary output in the 3D mode to generate timestamps. Strong ambient light is rejected by switching the image sensor to a 3D logarithmic mode from a 3D linear mode.