xdralex

@xdralex

Private team

Joined on May 18, 2021

  • [arxiv, papers with code] Summary Approach Pixel2Mesh is an end-to-end network, which takes an RGB 2D input image and produces a triangular 3D mesh by performing a few sequential mesh refinements applied to the initial ellipsoid mesh. The initial mesh is an ellipsoid with fixed number of vertices (156), fixed axis radiuses (0.2m, 0.2m, 0.4m), and fixed relative location from the camera (0.8m). As a note, it is unclear from the work whether camera intrinsics are fixed across the training examples. Throughout the paper, the mesh shape is considered to be a graph where mesh vertices are graph vertices, mesh edges are graph edges, and each vertex carries a feature vector in addition to the coordinates. These feature vectors are used in the graph convolution network described later.
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  • [arxiv, papers with code, supplement] Summary Approach Instead of reconstructing a 3D shape in the form of a (discrete) voxel grid, point cloud, or mesh from the input data, occupancy networks return a function that predicts an occupancy probability for a continuous 3D point in R3: The trick is the following: a function that takes an observation x as input and returns a function mapping a 3D point p to the occupancy probability can be replaced by a function that takes a pair (p, x) and returns the occupancy probability (see also: uncurrying): Limitations of existing 3D data representations
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