A method and a system including at least three image capturing devices for generating depth information are proposed. Multiple depth maps associated with a specific scene are obtained, where each of the depth maps corresponds to a different group of the image capturing devices and a different estimated region of the specific scene. For each pixel corresponding to the specific scene, whether the pixel is within a joint overlapping region of its estimated region is determined. If no, the depth information of the pixel is set according to its depth value in the depth map corresponding to a non-joint overlapping region of its estimated region. If yes, the depth information of the pixel is set according to its depth values in the depth maps corresponding to the joint overlapping region within its estimated region. An integrated depth map is generated by using the depth information of all the pixels.

A target view to a 3D scene depicted by a multiview image is determined. The multiview image comprising sampled views at sampled view positions distributed throughout a viewing volume. Each sampled view in the sampled views comprises a wide-field-of-view (WFOV) image and a WFOV depth map as seen from a respective sampled view position in the sampled view positions. The target view is used to select, from the sampled views, a set of sampled views. A display image is caused to be rendered on a display of a wearable device. The display image is generated based on a WFOV image and a WFOV depth map for each sampled view in the set of sampled views.

Various embodiments are disclosed for detecting, tracking and counting objects of interest in video. In an embodiment, a method of detecting and tracking objects of interest comprises: obtaining, by a computing device, multiple frames of images from an image capturing device; detecting, by the computing device, objects of interest in each frame; accumulating, by the computing device, multiple frames of object detections; creating, by the computing device, object tracks based on a batch of object detections over multiple frames; and associating, by the computing device, the object tracks over consecutive batches.

Disclosed is a stereo camera-based autonomous driving method and apparatus, the method including estimating a driving situation of a vehicle, determining a parameter to control a stereo camera width of a stereo camera based on the estimated driving situation, controlling a capturer configured to control arrangement between two cameras of the stereo camera for a first direction based on the determined parameter, and measuring a depth of an object located in the first direction based on two images respectively captured by the two cameras with the controlled arrangement.

Embodiments described herein provide various examples of an automatic obstacle avoidance system for unmanned vehicles using embedded stereo vision techniques. In one aspect, an UAV capable of performing autonomous obstacle detection and avoidance is disclosed. This UAV includes: a stereo vision camera set coupled to the one or more processors and the memory to capture a sequence of stereo images; and a stereo vision module configured to: receive a pair of stereo images captured by a pair of stereo vision cameras; perform a border cropping operation on the pair of stereo images to obtain a pair of cropped stereo images; perform a subsampling operation on the pair of cropped stereo images to obtain a pair of subsampled stereo images; and perform a dense stereo matching operation on the pair of subsampled stereo images to generate a dense three-dimensional (3D) point map of a space corresponding to the pair of stereo images.

Systems and methods in accordance with embodiments of the invention are configured to render images using light field image files containing an image synthesized from light field image data and metadata describing the image that includes a depth map. One embodiment of the invention includes a processor and memory containing a rendering application and a light field image file including an encoded image, a set of low resolution images, and metadata describing the encoded image, where the metadata comprises a depth map that specifies depths from the reference viewpoint for pixels in the encoded image. In addition, the rendering application configures the processor to: locate the encoded image within the light field image file; decode the encoded image; locate the metadata within the light field image file; and post process the decoded image by modifying the pixels based on the depths indicated within the depth map and the set of low resolution images to create a rendered image.

A gesture operation method based on depth values and the system thereof are revealed. A stereoscopic-image camera module acquires a first stereoscopic image. Then an algorithm is performed to judge if the first stereoscopic image includes a triggering gesture. Then the stereoscopic-image camera module acquires a second stereoscopic image. Another algorithm is performed to judge if the second stereoscopic image includes a command gesture for performing the corresponding operation of the command gesture.

Systems, methods, and machine-readable media for determining a three-dimensional environment model of the environment of one or more camera devices, in which image processing for inferring the model may be performed at the camera devices, are described.

An image processing apparatus that calculates three-dimensional information on a subject by a corresponding point search by taking one of three or more disparity images including the common subject to be a base image and one of the disparity images other than the base image to be a reference image, and includes: a search unit configured to search corresponding points base on an evaluation value in relation to a luminance gradient on an epi-polar line in each of the disparity images other the base image.

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.