Google Inc.

Kyle Genova

Forrester H Cole

Sofien Bouaziz

Geoffrey Everest Hinton

Andrea Tagliasacchi

Boyang Deng

Soroosh Yazdani

Daniel Vlasic

Systems and methods of the present disclosure are directed to a method that can include obtaining a 3D mesh comprising polygons and texture/shading data. The method can include rasterizing the 3D mesh to obtain a 2D raster comprising pixels and coordinates respectively associated with a subset of pixels. The method can include determining an initial color value for the subset of pixels based on the coordinates of the pixel and the associated shading/texture data. The method can include constructing a splat at the coordinates of a respective pixel. The method can include determining an updated color value for a respective pixel based on a weighting of the subset of splats to generate a 2D rendering of the 3D mesh based on the coordinates of a pixel and a splat.

Systems and methods for generating splat-based differentiable two-dimensional renderings

Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for generating convex decomposition of objects using neural network models. One of the methods includes receiving an input that depicts an object. The input is processed using a neural network to generate an output that defines a convex representation of the object. The output includes, for each of a plurality of convex elements, respective parameters that define a position of the convex element in the convex representation of the object.

Convex representation of objects using neural network

Methods, systems, and apparatus for obtaining first image features derived from an image of an object, providing the first image features to a three-dimensional estimator neural network, and obtaining, from the three-dimensional estimator neural network, data specifying an estimated three-dimensional shape and texture based on the first image features. The estimated three-dimensional shape and texture are provided to a three-dimensional rendering engine, and a plurality of three-dimensional views of the object are generated by the three-dimensional rendering engine based on the estimated three-dimensional shape and texture. The plurality of three-dimensional views are provided to the object recognition engine, and second image features derived from the plurality of three-dimensional views are obtained from the object recognition engine. A loss is computed based at least on the first and second image features, and the three-dimensional estimator neural network is trained based at least on the computed loss.

Learning to reconstruct 3D shapes by rendering many 3D views

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Princeton, NJ, United States of America

Kyle Genova