2022

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

Ma, Xu, Qin, Can, You, Haoxuan et al.

Understand

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.

Built on

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