Abstract
Deep learning on the point cloud is increasingly developing. Grouping the point with its neighbors and conducting convolution-like operation on them can learn the local feature of the point cloud, but this method is weak to extract the long-distance global feature. Performing the attention-based transformer on the whole point cloud can effectively learn the global feature of it, but this method is hardly to extract the local detailed feature. In this paper, we propose a novel module that can simultaneously extract and fuse local and global features, which is named as CT-block. The CT-block is composed of two branches, where the letter C represents the convolution-branch and the letter T represents the transformer-branch. The convolution-branch performs convolution on the grouped neighbor points to extract the local feature. Meanwhile, the transformer-branch performs offset-attention process on the whole point cloud to extract the global feature. Through the bridge constructed by the feature transmission element in the CT-block, the local and global features guide each other during learning and are fused effectively. We apply the CT-block to construct point cloud classification and segmentation networks, and evaluate the performance of them by several public datasets. The experimental results show that, because the features learned by CT-block are much expressive, the performance of the networks constructed by the CT-block on the point cloud classification and segmentation tasks achieve state of the art.
Abstract (translated)
URL
https://arxiv.org/abs/2111.15400
