Uatc, LLC

Jerry Junkai Liu

Raquel Urtasun

Shenlong Wang

Wei-Chiu Ma

Wenyuan Zeng

Kelvin Ka Wing Wong

Mehmet Ersin Yumer

Yushu Huang

Sourav Biswas

Example aspects of the present disclosure describe determining, using a machine-learned model framework, a motion trajectory for an autonomous platform. The motion trajectory can be determined based at least in part on a plurality of costs based at least in part on a distribution of probabilities determined conditioned on the motion trajectory.

Systems and methods for interactive prediction and planning

The present disclosure is directed encoding LIDAR point cloud data. In particular, a computing system can receive point cloud data for a three-dimensional space. The computing system can generate a tree-based data structure from the point cloud data, the tree-based data structure comprising a plurality of nodes. The computing system can generate a serial representation of the tree-based data structure. The computing system can, for each respective node represented by a symbol in the serial representation: determine contextual information for the respective node, generate, using the contextual information as input to a machine-learned model, a statistical distribution associated with the respective node, and generate a compressed representation of the symbol associated with the respective node by encoding the symbol using the statistical distribution for the respective node. The computing system can generate a compressed bitstream by sequentially ordering a plurality of compressed representations associated with the plurality of symbols.

System and methods for encoding octree structured point cloud data using an entropy model

Systems and method for video compression using conditional entropy coding. An ordered sequence of image frames can be transformed to produce an entropy coding for each image frame. Each of the entropy codings provide a compressed form of image information based on a prior image frame and a current image frame (the current image frame occurring after the prior image frame). In this manner, the compression model can capture temporal relationships between image frames or encoded representations of the image frames using a conditional entropy encoder trained to approximate the joint entropy between frames in the image frame sequence.

Conditional entropy coding for efficient video compression

A machine-learned image compression model includes a first encoder configured to generate a first image code based at least in part on first image data. The first encoder includes a first series of convolutional layers configured to generate a first series of respective feature maps based at least in part on the first image. A second encoder is configured to generate a second image code based at least in part on second image data and includes a second series of convolutional layers configured to generate a second series of respective feature maps based at least in part on the second image and disparity-warped feature data. Respective parametric skip functions associated convolutional layers of the second series are configured to generate disparity-warped feature data based at least in part on disparity associated with the first series of respective feature maps and the second series of respective feature maps.

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Toronto, Canada

Jerry Junkai Liu