Dynamic Local Feature Aggregation for Learning on Point Clouds
- URL: http://arxiv.org/abs/2301.02836v1
- Date: Sat, 7 Jan 2023 12:18:08 GMT
- Title: Dynamic Local Feature Aggregation for Learning on Point Clouds
- Authors: Zihao Li, Pan Gao, Hui Yuan, Ran Wei
- Abstract summary: We propose a dynamic feature aggregation (DFA) method that can transfer information by constructing local graphs in the feature domain without spatial constraints.
We demonstrate the superiority of our method by conducting extensive experiments on point cloud classification and segmentation tasks.
- Score: 15.595200007614274
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Existing point cloud learning methods aggregate features from neighbouring
points relying on constructing graph in the spatial domain, which results in
feature update for each point based on spatially-fixed neighbours throughout
layers. In this paper, we propose a dynamic feature aggregation (DFA) method
that can transfer information by constructing local graphs in the feature
domain without spatial constraints. By finding k-nearest neighbors in the
feature domain, we perform relative position encoding and semantic feature
encoding to explore latent position and feature similarity information,
respectively, so that rich local features can be learned. At the same time, we
also learn low-dimensional global features from the original point cloud for
enhancing feature representation. Between DFA layers, we dynamically update the
constructed local graph structure, so that we can learn richer information,
which greatly improves adaptability and efficiency. We demonstrate the
superiority of our method by conducting extensive experiments on point cloud
classification and segmentation tasks. Implementation code is available:
https://github.com/jiamang/DFA.
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