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Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling. (arXiv:2205.09962v1 [cs.CV])
May 23, 2022, 1:12 a.m. | Kevin Tirta Wijaya, Dong-Hee Paek, Seung-Hyun Kong
cs.CV updates on arXiv.org arxiv.org
Existing point cloud feature learning networks often incorporate sequences of
sampling, neighborhood grouping, neighborhood-wise feature learning, and
feature aggregation to learn high-semantic point features that represent the
global context of a point cloud. Unfortunately, the compounded loss of
information concerning granularity and non-maximum point features due to
sampling and max pooling could adversely affect the high-semantic point
features from existing networks such that they are insufficient to represent
the local context of a point cloud, which in turn may hinder …
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