POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing

• URL: http://arxiv.org/abs/2111.13386v1
• Date: Fri, 26 Nov 2021 09:45:01 GMT
• Title: POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing
• Authors: Sheng Xu, Yanjing Li, Junhe Zhao, Baochang Zhang, Guodong Guo
• Abstract summary: We introduce point-wise operations based on Expectation-Maximization into BNNs for efficient point cloud processing. Our POEM surpasses existing the state-of-the-art binary point cloud networks by a significant margin, up to 6.7 %.
• Score: 53.74076015905961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address this issue, we implement XNOR-Net-based binary neural networks (BNNs) for an efficient point cloud processing, but its performance is severely suffered due to two main drawbacks, Gaussian-distributed weights and non-learnable scale factor. In this paper, we introduce point-wise operations based on Expectation-Maximization (POEM) into BNNs for efficient point cloud processing. The EM algorithm can efficiently constrain weights for a robust bi-modal distribution. We lead a well-designed reconstruction loss to calculate learnable scale factors to enhance the representation capacity of 1-bit fully-connected (Bi-FC) layers. Extensive experiments demonstrate that our POEM surpasses existing the state-of-the-art binary point cloud networks by a significant margin, up to 6.7 %.

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