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 a novel perspective on this task. We notice that detailed local
geometrical information probably is not the key to point cloud analysis -- we
introduce a pure residual MLP network, called PointMLP, which integrates no
sophisticated local geometrical extractors but still performs very
competitively. Equipped with a proposed lightweight geometric affine module,
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 that PointMLP achieves this strong
performance without any sophisticated operations, hence leading to a superior
inference speed. Compared to most recent CurveNet, PointMLP trains 2x faster,
tests 7x 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 this https URL.

本文提出了一种名为 PointMLP 的纯残差 MLP 网络，它不使用复杂的局部几何提取器，但仍能表现出很高的竞争力，装备了提出的轻量级几何仿射模块，PointMLP 在多个数据集上都达到了新的最佳状态，相对于大多数最近的 CurveNet，PointMLP 的训练速度快两倍，测试速度快七倍，并在 ModelNet40 基准测试中获得更高的精度。