LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object
  Detection
Sifan Zhou Liang Li Xinyu Zhang Bo Zhang Shipeng Bai Miao Sun Ziyu Zhao Xiaobo Lu Xiangxiang Chu
Due to highly constrained computing power and memory, deploying 3D
lidar-based detectors on edge devices equipped in autonomous vehicles and
robots poses a crucial challenge. Being a convenient and straightforward model
compression approach, Post-Training Quantization (PTQ) has been widely adopted
in 2D vision tasks. However, applying it directly to 3D lidar-based tasks
inevitably leads to performance degradation. As a remedy, we propose an
effective PTQ method called LiDAR-PTQ, which is particularly curated for 3D
lidar detection (both SPConv-based and SPConv-free). Our LiDAR-PTQ features
three main components, \textbf{(1)} a sparsity-based calibration method to
determine the initialization of quantization parameters, \textbf{(2)} a
Task-guided Global Positive Loss (TGPL) to reduce the disparity between the
final predictions before and after quantization, \textbf{(3)} an adaptive
rounding-to-nearest operation to minimize the layerwise reconstruction error.
Extensive experiments demonstrate that our LiDAR-PTQ can achieve
state-of-the-art quantization performance when applied to CenterPoint (both
Pillar-based and Voxel-based). To our knowledge, for the very first time in
lidar-based 3D detection tasks, the PTQ INT8 model's accuracy is almost the
same as the FP32 model while enjoying $3\times$ inference speedup. Moreover,
our LiDAR-PTQ is cost-effective being $30\times$ faster than the
quantization-aware training method. Code will be released at
\url{https://github.com/StiphyJay/LiDAR-PTQ}.