Adaptive Channel Encoding for Point Cloud Analysis
This addresses a bottleneck in point cloud analysis for applications like 3D vision, but it is incremental as it builds on existing attention mechanisms.
The paper tackles the problem of screening useful channel information in point cloud analysis by proposing an adaptive channel encoding mechanism, which improves representation quality and achieves state-of-the-art results on existing benchmarks.
Attention mechanism plays a more and more important role in point cloud analysis and channel attention is one of the hotspots. With so much channel information, it is difficult for neural networks to screen useful channel information. Thus, an adaptive channel encoding mechanism is proposed to capture channel relationships in this paper. It improves the quality of the representation generated by the network by explicitly encoding the interdependence between the channels of its features. Specifically, a channel-wise convolution (Channel-Conv) is proposed to adaptively learn the relationship between coordinates and features, so as to encode the channel. Different from the popular attention weight schemes, the Channel-Conv proposed in this paper realizes adaptability in convolution operation, rather than simply assigning different weights for channels. Extensive experiments on existing benchmarks verify our method achieves the state of the arts.