A depth map generation device for merging multiple depth maps includes at least three image capturers, a depth map generator, and a mixer. The at least three image capturers form at least two image capture pairs. The depth map generator is coupled to the at least three image capturers for generating a depth map corresponding to each image capturer pair of the at least two image capture pairs according to an image pair captured by the each image capturer. The mixer is coupled to the depth map generator for merging at least two depth maps corresponding to the at least two image capturer pairs to generate a final depth map, wherein the at least two depth maps have different characteristics.

The pose of an object may be estimated based on fiducial points identified in a visual representation of the object. Each fiducial point may correspond with a component of the object, and may be associated with a first location in an image of the object and a second location in a 3D space. A 3D skeleton of the object may be determined by connecting the locations in the 3D space, and the object's pose may be determined based on the 3D skeleton.

Techniques and systems are described herein for determining dynamic lighting for objects in images. Using such techniques and systems, a lighting condition of one or more captured images can be adjusted. Techniques and systems are also described herein for determining depth values for one or more objects in an image. In some cases, the depth values (and the lighting values) can be determined using only a single camera and a single image, in which case one or more depth sensors are not needed to produce the depth values.

A method and apparatus for sorting is described, and which includes providing a product stream formed of individual objects of interest having feature aspects which can be detected; generating multiple images of each of the respective objects of interest; classifying the feature aspects of the objects of interest; identifying complementary images by analyzing some of the multiplicity of images; fusing the complementary images to form an aggregated region representation of the complementary images; and sorting the respective objects of interest based at least in part upon the aggregated region representation which is formed.

The invention relates a method for generating a motion-corrected image for visual-inertial odometry comprising an event camera rigidly connected to an inertial measurement unit (IMU), wherein the event camera comprises pixels arranged in an image plane that are configured to output events in presence of brightness changes in a scene at the time they occur, wherein each event comprises the time at which it is recorded and a position of the respective pixel that detected the brightness change, the method comprising the steps of: Acquiring at least one set of events (S), wherein the at least one set (S) comprises a plurality of subsequent events (e); Acquiring IMU data (D) for the duration of the at least one set (S); Generating a motion-corrected image from the at least one set (S) of events (e), wherein the motion-corrected image is obtained by assigning the position (x.sub.j) of each event (e.sub.j) recorded at its corresponding event time (t.sub.j) at an estimated event camera pose (T.sub.tj) to an adjusted event position (x.sub.j), wherein the adjusted event position (x.sub.j) is obtained by determining the position of the event (e.sub.j) for an estimated reference camera pose (T.sub.tf.sub.k) at a reference time (.sub.tf.sub.k), wherein the estimated camera pose (T.sub.tj) at the event time (t.sub.j) and the reference camera pose (T.sub.tf.sub.k) at the reference time (.sub.tf.sub.k) are estimated by means of the IMU data (D).

This disclosure describes a configuration of an aerial vehicle, such as an unmanned aerial vehicle (UAV), that includes a plurality of cameras that may be selectively combined to form a stereo pair for use in obtaining stereo images that provide depth information corresponding to objects represented in those images. Depending on the distance between an object and the aerial vehicle, different cameras may be selected for the stereo pair based on the baseline between those cameras and a distance between the object and the aerial vehicle. For example, cameras with a small baseline (close together) may be selected to generate stereo images and depth information for an object that is close to the aerial vehicle. In comparison, cameras with a large baseline may be selected to generate stereo images and depth information for an object that is farther away from the aerial vehicle.

A depth map generation device for merging multiple depth maps includes two image capturers, a depth map generator, and a mixer. The two image capturers are used for generating two first images. The depth map generator is coupled to the two image capturers, wherein the depth map generator generates a first depth map and a second depth map according to the two first images. The mixer is coupled to the depth map generator for merging the first depth map and the second depth map to generate a final depth map, wherein the first depth map and the second depth map have different characteristics.

A 3D information calculation apparatus includes processing circuitry that may receive first and second images of different first and second wavelength bands, respectively, at a same time and angle of view based on a subject being imaged while structured light of the first wavelength band is projected on to subject, receive third and fourth images of the first and second wavelength bands, respectively, at a same time and angle of view based on the subject being imaged while the structured light is not projected on the subject, calculate a first difference image of the first wavelength band based on subtracting the first and third images, calculate a second difference image of the second wavelength band based on subtracting the second and fourth images, calculate an extraction image based on subtracting the first and second difference images, and calculate a distance to the subject based on the extraction image.

The disclosure relates to an image processing apparatus for determining a depth of a pixel of a reference image of a plurality of images representing a visual scene relative to a plurality of locations, wherein the plurality of locations define a two-dimensional grid with rows and columns and wherein the location of the reference image is associated with a reference row and a reference column of the grid. The image processing apparatus comprises a depth determiner configured to determine a first depth estimate on the basis of the reference image and a first subset of the plurality of images for determining the depth of the pixel of the reference image, wherein the images of the first subset are associated with locations being associated with a row of the grid different than the reference row and with a column of the grid different than the reference column.

The present disclosure relates to an information processing apparatus and an information processing method that are configured to be capable of efficiently acquiring information for use in generating three-dimensional data from two-dimensional image data. A grouping block sorts two or more virtual cameras for acquiring two-dimensional image data into two or more groups. A global table generation block generates a global table in which group information related with each of two or more groups is registered. A group table generation block generates, for each group, a group table in which camera information for use in generating three-dimensional data from two-dimensional image data acquired by a virtual camera sorted into a group is registered. The present disclosure is applicable to an encoding apparatus and the like, for example.