Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud, which plays a critical role in 3D computer vision. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. However, these methods often produce holes, outliers or non-uniformity due to the splitting and merging process which does not maintain consistency among local patches. To address these issues, we propose a novel approach that learns an unsigned distance field guided by local priors for point cloud upsampling. Specifically, we train a local distance indicator (LDI) that predicts the unsigned distance from a query point to a local implicit surface. Utilizing the learned LDI, we learn an unsigned distance field to represent the sparse point cloud with patch consistency. At inference time, we randomly sample queries around the sparse point cloud, and project these query points onto the zero-level set of the learned implicit field to generate a dense point cloud. We justify that the implicit field is naturally continuous, which inherently enables the application of arbitrary-scale upsampling without necessarily retraining for various scales. We conduct comprehensive experiments on both synthetic data and real scans, and report state-of-the-art results under widely used benchmarks.
Overview of our method. The key idea of our method is to learn an unsigned distance field to represent the sparse point cloud at global level with the guidance from a pre-trained attention-based local distance indicator learned from dense local patches. The attention-based distance indicator predicts the distance between the query point and the patch surface.
@inproceedings{li2024LDI,
title={Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling},
author={Li, Shujuan and Zhou, Junsheng and Ma, Baorui and Liu, Yu-Shen and Han, Zhizhong},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024}
}