LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
Authors: Sifan Zhou, Liang Li, Xinyu Zhang, Bo Zhang, Shipeng Bai, Miao Sun, Ziyu Zhao, Xiaobo Lu, Xiangxiang Chu
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our Li DAR-PTQ can achieve stateof-the-art quantization performance when applied to Center Point (both Pillar-based and Voxel-based). |
| Researcher Affiliation | Collaboration | 1Southeast University 2Meituan Inc 3Zhejiang University 4Nanyang Technological University |
| Pseudocode | Yes | We formulate the our Li DAR-PTQ algorithm for a full precision 3D detector in Algorithm 2. |
| Open Source Code | Yes | Code will be released at https://github.com/Stiphy Jay/Li DAR-PTQ. |
| Open Datasets | Yes | To evaluate the effectiveness of our proposed Lidar-PTQ, we conduct main experiments on large-scale autonomous driving datasets, Waymo Open Dataset (WOD) (Sun et al., 2020). |
| Dataset Splits | Yes | In WOD dataset, we randomly sample 256 frames point cloud data from the training set as the calibration data. The calibration set proportions is 0.16% (256/158,081) for WOD. In nu Scenes dataset, the calibration set proportions are 0.91% (256/28,130). |
| Hardware Specification | Yes | We execute all experiments on a single Nvidia Tesla V100 GPU. For the speed test, the inference time of all comparison methods is measured on an NVIDIA Jeston AGX Orin. |
| Software Dependencies | No | The paper mentions using 'Center Point(Yin et al., 2021) official open-source codes based on Det3D (Zhu et al., 2019) framework' but does not provide specific version numbers for software components or libraries. |
| Experiment Setup | Yes | The learning rate for the activation quantization scaling factor is 5e-5, and for weight quantization rounding is 5e-3. In TGPL loss, we set γ as 0.1, and K as 500. |