CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point
Cloud Learning
- URL: http://arxiv.org/abs/2208.00524v1
- Date: Sun, 31 Jul 2022 21:39:15 GMT
- Title: CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point
Cloud Learning
- Authors: Mahdi Saleh, Yige Wang, Nassir Navab, Benjamin Busam, Federico Tombari
- Abstract summary: We set transformers in this work and incorporate them into a hierarchical framework for shape classification and part and scene segmentation.
We also compute efficient and dynamic global cross attentions by leveraging sampling and grouping at each iteration.
The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods.
- Score: 81.85951026033787
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Processing 3D data efficiently has always been a challenge. Spatial
operations on large-scale point clouds, stored as sparse data, require extra
cost. Attracted by the success of transformers, researchers are using
multi-head attention for vision tasks. However, attention calculations in
transformers come with quadratic complexity in the number of inputs and miss
spatial intuition on sets like point clouds. We redesign set transformers in
this work and incorporate them into a hierarchical framework for shape
classification and part and scene segmentation. We propose our local attention
unit, which captures features in a spatial neighborhood. We also compute
efficient and dynamic global cross attentions by leveraging sampling and
grouping at each iteration. Finally, to mitigate the non-heterogeneity of point
clouds, we propose an efficient Multi-Scale Tokenization (MST), which extracts
scale-invariant tokens for attention operations. The proposed hierarchical
model achieves state-of-the-art shape classification in mean accuracy and
yields results on par with the previous segmentation methods while requiring
significantly fewer computations. Our proposed architecture predicts
segmentation labels with around half the latency and parameter count of the
previous most efficient method with comparable performance. The code is
available at https://github.com/YigeWang-WHU/CloudAttention.
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