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 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.
Moreover, BiPointNet gives an impressive 14.7x speedup and 18.9x storage saving
on real-world resource-constrained devices.

本文提出了一种针对点云运算的模型二值化方法 BiPointNet，该方法引入了 Entropy-Maximizing Aggregation 和 Layer-wise Scale Recovery 来解决聚合引起的特征同质化和尺度失真问题，并在多个基本任务和主流骨干网络上实现了显著的改善，且在实际应用中取得了显著的性能提升效果，包括 14.7 倍的加速和 18.9 倍的存储节省。