PAD: Detail-Preserving Point Cloud Reconstruction and Generation via Autodecoders

Abstract

High-accuracy point cloud (self-) reconstruction is crucial for point cloud editing, translation, and unsupervised representation learning. However, existing point cloud reconstruction methods often sacrifice many geometric details. Altough many techniques have proposed how to construct better point cloud decoders, only a few have designed point cloud encoders from a reconstruction perspective. We propose an autodecoder architecture to achieve detail-preserving point cloud reconstruction while bypassing the performance bottleneck of the encoder. Our architecture is theoretically applicable to any existing point cloud decoder. For training, both the weights of the decoder and the pre-initialised latent codes, corresponding to the input points, are updated simultaneously. Experimental results demonstrate that our autodecoder achieves an average reduction of 24.62% in Chamfer Distance compared to existing methods, significantly improving reconstruction quality on the ShapeNet dataset. Furthermore, we verify the effectiveness of our autodecoder in point cloud generation, upsampling, and unsupervised representation learning to demonstrate its performance on downstream tasks, which is comparable to the state-of-the-art methods. We will make our code publicly available after peer review.