Abstract: Aiming at the real-time requirement of 3D point cloud detection algorithms in autonomous driving scenarios, INT8 quantization is a mainstream lightweight solution, but traditional quantization methods in the image field suffer from severe accuracy loss when applied to point cloud algorithms. Taking the PointPillars algorithm as the research object, through quantitative sensitive layer analysis, it is found that the pillar feature net (PFN) is the core bottleneck of quantization accuracy loss, and its input activation values show the distribution characteristics of large dynamic range difference and many outliers. Drawing on the distribution optimization idea of large models, this paper proposes to introduce an root mean square layer normalization (RMSNorm ) at the input end of the PFN layer and perform fine-tuning to compress the feature dynamic range into an INT8 quantization-friendly interval. Experiments show that this method reduces the mean average precision (mAP) loss of the PointPillars model after INT8 quantization from 13.7% to 1.4% on the NuScenes dataset, increases the inference speed by 3.1 times, and compresses the model volume by 75%. Moreover, this scheme is also effective on the CenterPoint algorithm, controlling the mAP loss within 1.8%. The research provides a feasible path for efficient quantization of 3D point cloud detection models and lays a foundation for ultra-low bit quantization.

Key words: 3D point cloud detection; PointPillars algorithm; INT8 quantization; post-training quantization; RMSNorm; autonomous driving

摘要： 针对自动驾驶场景下 3D 点云检测算法的实时性需求，INT8 量化是主流轻量化方案， 但传统图像领域量化方法在点云算法上存在精度骤降问题。以 PointPillars 算法为研究对象， 通过量化敏感层分析发现，Pillar 特征提取网络（PFN）是量化精度损失的核心瓶颈，其输入 激活值呈现动态范围差异大、离群值多的分布特性。借鉴大模型分布优化思路，提出在 PFN 层输入端引入均方根层归一化（RMSNorm）并进行微调，将特征动态范围压缩至 INT8 量化 友好区间。实验表明，该方法在 NuScenes 数据集上使 PointPillars 模型 INT8 量化后的平均精 度均值（mAP）损失从 13.7%降至 1.4%，推理速度提升 3.1 倍，模型体积压缩 75%；且该方 案在 CenterPoint 算法上同样有效，mAP 损失控制在 1.8%以内。研究为 3D 点云检测模型的高 效量化提供了可行路径，也为超低比特量化奠定基础。

关键词: 3D 点云检测；PointPillars 算法；INT8 量化；后训练量化；RMSNorm；自动驾驶