  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}.
