PointMLP：Rethinking Network Design and Local Geometry in Point Cloud： A Simple Residual MLP Framework . ICLR 2022
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1. 四个问题

解决什么问题

点云随着CV领域的发展而发展（毕竟点云属于CV领域），先前的工作主要从设计局部特征提取器出发，所用方法有convolution, graph, or attention mechanisms。这些精妙设计损害了推理速度，而且近些年性能也饱和了。
这篇paper不再从 exploring sophisticated local geometric extractors出发，只用带残差连接的MLP模块堆叠，大道至简。

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

We find 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 local geometrical extractors but still performs very competitively.

用什么方法解决

带残差连接的MLP模块

效果如何

在3D Point Cloud Classification的ModelNet40和ScanObjectNN benchmark都取得第一，在3D shape part segmentation task on the ShapeNetPart benchmark 领域还差一点（a competitive performance）

ModelNet40 达到准确率94.5，比第二名CurveNet超出0.3%（94.5% vs. 94.2%），作者说这是一个 promising achievement，因为ModelNet40的结果已经在94%停留了很长时间了。

Experimental results are presented in Table 2.
Among these methods, our PointMLP clearly outperforms state-of-the-art method CurveNet by 0.3% (94.5% vs. 94.2%) overall accuracy with only 1k points.
Note that this improvement could be considered as a promising achievement since the results on ModelNet40 recent methods have been saturated around 94% for a long time.
Even without the voting strategy (Liu et al., 2019b), our PointMLP still performs on par or even better than other methods that are tested with voting strategy.