# BiPointNet: Binary Neural Network for Point Clouds

arXiv: https://arxiv.org/abs/2010.05501 · HTML: https://mlsanity.com/paper/2010.05501/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 30 of 30)

- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [Diversifying Sample Generation for Accurate Data-Free Quantization](https://mlsanity.com/paper/2103.01049/) (2021)
- [A comprehensive review of Binary Neural Network](https://mlsanity.com/paper/2110.06804/) (2021)
- [Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection](https://mlsanity.com/paper/2108.09917/) (2021)
- [LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection](https://mlsanity.com/paper/2401.15865/) (2024)
- [Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution](https://mlsanity.com/paper/2303.08942/) (2023)
- [Distribution-sensitive Information Retention for Accurate Binary Neural Network](https://mlsanity.com/paper/2109.12338/) (2021)
- [BiBench: Benchmarking and Analyzing Network Binarization](https://mlsanity.com/paper/2301.11233/) (2023)
- [SiMaN: Sign-to-Magnitude Network Binarization](https://mlsanity.com/paper/2102.07981/) (2021)
- [Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encoder](https://mlsanity.com/paper/2405.18734/) (2024)
- [Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization](https://mlsanity.com/paper/2109.00212/) (2021)
- [SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation](https://mlsanity.com/paper/2209.05924/) (2022)
- [Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification](https://mlsanity.com/paper/2108.06317/) (2021)
- [PointHPS: Cascaded 3D Human Pose and Shape Estimation from Point Clouds](https://mlsanity.com/paper/2308.14492/) (2023)
- [Efficient Multitask Dense Predictor via Binarization](https://mlsanity.com/paper/2405.14136/) (2024)
- [Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications](https://mlsanity.com/paper/2508.15008/) (2025)
- [POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing](https://mlsanity.com/paper/2111.13386/) (2021)
- [BiFSMN: Binary Neural Network for Keyword Spotting](https://mlsanity.com/paper/2202.06483/) (2022)
- [Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud](https://mlsanity.com/paper/2209.08938/) (2022)
- [BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance](https://mlsanity.com/paper/2211.06987/) (2022)
- [Multi-Pretext Attention Network for Few-shot Learning with Self-supervision](https://mlsanity.com/paper/2103.05985/) (2021)
- [Hardware-friendly Deep Learning by Network Quantization and Binarization](https://mlsanity.com/paper/2112.00737/) (2021)
- [Model and Data Agreement for Learning with Noisy Labels](https://mlsanity.com/paper/2212.01054/) (2022)
- [Deep Lookup Network](https://mlsanity.com/paper/2509.13662/) (2025)
- [BPT: Binary Point Cloud Transformer for Place Recognition](https://mlsanity.com/paper/2303.01166/) (2023)
- [Binarizing Sparse Convolutional Networks for Efficient Point Cloud Analysis](https://mlsanity.com/paper/2303.15493/) (2023)
- [FBPT: A Fully Binary Point Transformer](https://mlsanity.com/paper/2403.09998/) (2024)
- [NAS-BNN: Neural Architecture Search for Binary Neural Networks](https://mlsanity.com/paper/2408.15484/) (2024)
- [CycleBNN: Cyclic Precision Training in Binary Neural Networks](https://mlsanity.com/paper/2410.00050/) (2024)

## Abstract (arXiv)

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.