This project implements a neural network model for reconstructing 3D surfaces, based on the DeepSDF architecture from CVPR 2019. It uses a deep, fully-connected neural network (Decoder) to learn Signed Distance Functions (SDFs) from 3D point clouds.
3D-shape compression with a shape-coded auto-decoder and sinusoidal activations.
DeepSDF Generative Model applied to the problem of exploratory design in Architectural Tower Design
Simple implementation of DeepSDF
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