Surface Reconstruction from Point Clouds: A Survey and a Benchmark

TL;DR

This paper provides a comprehensive survey and benchmark of surface reconstruction methods from point clouds, highlighting the performance of classical and deep learning approaches under various practical sensing imperfections.

Contribution

It introduces a large-scale benchmark dataset with synthetic and real data, and conducts empirical studies comparing methods' robustness and generalization in surface reconstruction.

Findings

Classical methods often outperform deep learning in robustness and generalization.

Deep learning methods are increasingly popular but not always superior.

Challenges like misalignment, missing points, and outliers remain unsolved.

Abstract

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided. However, existing reviews are short of thorough investigations on a common benchmark. The present paper aims to review and benchmark existing methods in the new era of deep learning surface reconstruction. To this end, we contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object- and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark, and pay special attention on robustness of existing methods against various scanning imperfections; we also study how different methods generalize in terms of reconstructing complex surface shapes. Our studies help identify the best conditions under which different methods work, and suggest some empirical findings. For example, while deep learning methods are increasingly popular, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods. We expect that the benchmark and our studies would be valuable both for practitioners and as a guidance for new innovations in future research.