Hierarchical Point Cloud Encoding and Decoding with Lightweight Self-Attention based Model

• URL: http://arxiv.org/abs/2202.06407v1
• Date: Sun, 13 Feb 2022 21:10:06 GMT
• Title: Hierarchical Point Cloud Encoding and Decoding with Lightweight Self-Attention based Model
• Authors: En Yen Puang, Hao Zhang, Hongyuan Zhu, Wei Jing
• Abstract summary: SA-CNN is a self-attention based encoding and decoding architecture for representation learning of point cloud data. We demonstrate that SA-CNN is capable of a wide range of applications, namely classification, part segmentation, reconstruction, shape retrieval, and unsupervised classification.
• Score: 22.338247335791095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present SA-CNN, a hierarchical and lightweight self-attention based encoding and decoding architecture for representation learning of point cloud data. The proposed SA-CNN introduces convolution and transposed convolution stacks to capture and generate contextual information among unordered 3D points. Following conventional hierarchical pipeline, the encoding process extracts feature in local-to-global manner, while the decoding process generates feature and point cloud in coarse-to-fine, multi-resolution stages. We demonstrate that SA-CNN is capable of a wide range of applications, namely classification, part segmentation, reconstruction, shape retrieval, and unsupervised classification. While achieving the state-of-the-art or comparable performance in the benchmarks, SA-CNN maintains its model complexity several order of magnitude lower than the others. In term of qualitative results, we visualize the multi-stage point cloud reconstructions and latent walks on rigid objects as well as deformable non-rigid human and robot models.

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