Qualcomm Incorporated

Khalid Tahboub

Bing Han

Marta Karczewicz

Geert Van Der Auwera

Louis Joseph Kerofsky

Fatih Murat Porikli

Kai Wang

Yasutomo Matsuba

Hong Cai

Risheek Garrepalli

Jisoo Jeong

Kuan-Ting Shen

Dangdang Shao

Hua-I Chang

Mathew Sam

A device includes a decoder that includes a spatial prediction engine, a temporal prediction engine, a reconstruction engine, and a decoded picture buffer. The device also includes a controller configured to cause the decoder to, in a base resolution mode, reconstruct a base resolution version of a block of a frame using the reconstruction engine and at least one of the spatial prediction engine or the temporal prediction engine. The controller is also configured to cause the decoder to, in an enhanced resolution mode, generate an enhanced resolution version of the block using the reconstruction engine and the at least one of the spatial prediction engine or the temporal prediction engine.

Enhanced resolution generation at decoder

Systems and techniques are provided for generating disparity information from two or more images. For example, a process can include obtaining first disparity information corresponding to a pair of images, the pair of images including a first image of a scene and a second image of the scene. The process can include obtaining confidence information associated with the first disparity information. The process can include processing, using a machine learning network, the first disparity information and the confidence information to generate second disparity information corresponding to the pair of images. The process can include combining, based on the confidence information, the first disparity information with the second disparity information to generate a refined disparity map corresponding to the pair of images.

Disparity-based depth refinement using confidence information and stereoscopic depth information

Certain aspects of the present disclosure provide techniques for global motion modeling. Embodiments include receiving a first image and a second image from a camera attached to a moving object. Embodiments include identifying a pixel in the first image. Embodiments include determining, based on one or more parameters associated with the camera, a vector representing a range of locations in which a real-world point corresponding to the pixel is likely to be found in the second image, wherein the parameters associated with the camera comprise: a first parameter related to a location of the camera relative to a ground surface; a second parameter related to motion of the moving object; and a third parameter related to an orientation of the camera relative to the ground surface. Embodiments include determining, using the vector, a location of the real-world point in the second image.

Global motion modeling for automotive image data

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San Diego, CA, United States of America

Khalid Tahboub