DDSL: Deep Differentiable Simplex Layer for Learning Geometric Signals

ICCV 2019 Chiyu "Max" JiangDana Lynn Ona LansiganPhilip MarcusMatthias Nießner

We present a Deep Differentiable Simplex Layer (DDSL) for neural networks for geometric deep learning. The DDSL is a differentiable layer compatible with deep neural networks for bridging simplex mesh-based geometry representations (point clouds, line mesh, triangular mesh, tetrahedral mesh) with raster images (e.g., 2D/3D grids)... (read more)

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