Learning to Train a Point Cloud Reconstruction Network Without Matching

Abstract

Reconstruction networks for well-ordered data such as 2D images and 1D continuous signals are easy to optimize through element-wised squared errors, while permutation-arbitrary point clouds cannot be constrained directly because their points permutations are not fixed. Though existing works design algorithms to match two point clouds and evaluate shape errors based on matched results, they are limited by pre-defined matching processes. In this work, we propose a novel framework named PCLossNet which learns to train a point cloud reconstruction network without any matching. By training through an adversarial process together with the reconstruction network, PCLossNet can better explore the differences between point clouds and create more precise reconstruction results. Experiments on multiple datasets prove the superiority of our method, where PCLossNet can help networks achieve much lower reconstruction errors and extract more representative features, with about 4 times faster training efficiency than the commonly-used EMD loss. Our codes can be found in https://github.com/Tianxinhuang/PCLossNet.