Decoupled Local Aggregation for Point Cloud Learning
Binjie Chen Yunzhou Xia Yu Zang Cheng Wang Jonathan Li
The unstructured nature of point clouds demands that local aggregation be
adaptive to different local structures. Previous methods meet this by
explicitly embedding spatial relations into each aggregation process. Although
this coupled approach has been shown effective in generating clear semantics,
aggregation can be greatly slowed down due to repeated relation learning and
redundant computation to mix directional and point features. In this work, we
propose to decouple the explicit modelling of spatial relations from local
aggregation. We theoretically prove that basic neighbor pooling operations can
too function without loss of clarity in feature fusion, so long as essential
spatial information has been encoded in point features. As an instantiation of
decoupled local aggregation, we present DeLA, a lightweight point network,
where in each learning stage relative spatial encodings are first formed, and
only pointwise convolutions plus edge max-pooling are used for local
aggregation then. Further, a regularization term is employed to reduce
potential ambiguity through the prediction of relative coordinates.
Conceptually simple though, experimental results on five classic benchmarks
demonstrate that DeLA achieves state-of-the-art performance with reduced or
comparable latency. Specifically, DeLA achieves over 90\% overall accuracy on
ScanObjectNN and 74\% mIoU on S3DIS Area 5. Our code is available at
https://github.com/Matrix-ASC/DeLA .