BiPointNet: Binary Neural Network for Point Clouds

Haotong Qin*
Zhongang Cai*
Mingyuan Zhang*
Yifu Ding

Haiyu Zhao
Shuai Yi
Xianglong Liu
Hao Su

Beihang University
SenseTime Research
UC San Diego

Accepted in ICLR 2021

To alleviate the resource constraint for real-time point cloud applications that run on edge devices, in this paper we present BiPointNet, the first model binarization approach for efficient deep learning on point clouds. We first discover that the immense performance drop of binarized models for point clouds mainly stems from two challenges: aggregation-induced feature homogenization that leads to a degradation of information entropy, and scale distortion that hinders optimization and invalidates scale-sensitive structures. With theoretical justifications and in-depth analysis, our BiPointNet introduces Entropy-Maximizing Aggregation (EMA) to modulate the distribution before aggregation for the maximum information entropy, and Layer-wise Scale Recovery (LSR) to efficiently restore feature representation capacity. Extensive experiments show that BiPointNet outperforms existing binarization methods by convincing margins, at the level even comparable with the full precision counterpart. We highlight that our techniques are generic, guaranteeing significant improvements on various fundamental tasks and mainstream backbones, e.g., BiPointNet gives an impressive 14.7× speedup and 18.9× storage saving on real-world resource-constrained devices.

Paper and Code




Deployment Efficiency on ARM Devices


This work was supported by National Natural Science Foundation of China (62022009, 61872021), Beijing Nova Program of Science and Technology (Z191100001119050), and State Key Lab of Software Development Environment (SKLSDE-2020ZX-06).

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