Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP FrameworkDownload PDF

Published: 28 Jan 2022, Last Modified: 22 Oct 2023ICLR 2022 PosterReaders: Everyone
Keywords: point cloud representation, local relation, mlp
Abstract: Point cloud analysis is challenging due to irregularity and unordered data structure. To capture the 3D geometries, prior works mainly rely on exploring sophisticated local geometric extractors, using convolution, graph, or attention mechanisms. These methods, however, incur unfavorable latency during inference and the performance saturates over the past few years. In this paper, we present an ovel perspective on this task. We find detailed local geometrical informationprobably is not the key to point cloud analysis – we introduce a pure residual MLP network, called PointMLP, which integrates no local geometrical extractors but still performs very competitively. Equipped with a proposed lightweight geometric-affine module to stabilize the training, PointMLP delivers the new state-of-the-art on multiple datasets. On the real-world ScanObjectNN dataset, our method even surpasses the prior best method by 3.3% accuracy. We emphasize PointMLP achieves this strong performance without any sophisticated operations, hence leading to a prominent inference speed. Compared to most recent CurveNet, PointMLP trains 2× faster, tests 7× faster, and is more accurate on ModelNet40 benchmark. We hope our PointMLP may help the community towards a better understanding of point cloud analysis. The code is available at
One-sentence Summary: In this paper, we present a new design for point cloud analysis , dubbed as PointMLP, which delivers new state-of-the-art results on multiple benchmarks and exhibits gratifying inference speed.
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