AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset
Authors: Jiakang Yuan, Bo Zhang, Xiangchao Yan, Botian Shi, Tao Chen, Yikang LI, Yu Qiao
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By conducting extensive experiments, we argue that there are two key issues that need to be solved for achieving the real AD-PT... Our study provides a more unified approach, meaning that once the pre-trained checkpoint is generated, it can be directly loaded into multiple perception baselines and benchmarks. Results further verify that such an AD-PT paradigm achieves large accuracy gains on different benchmarks (e.g., 3.41%, 8.45%, 4.25% on Waymo, nu Scenes, and KITTI). |
| Researcher Affiliation | Collaboration | Jiakang Yuan1, , Bo Zhang2, , Xiangchao Yan2, Tao Chen1, , Botian Shi2, Yikang Li2, Yu Qiao2 1School of Information Science and Technology, Fudan University 2Shanghai Artificial Intelligence Laboratory |
| Pseudocode | No | The paper describes the model architecture and mathematical formulations but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Project page: https://jiakangyuan.github.io/AD-PT.github.io/. Our code is based on 3DTrans [22]. |
| Open Datasets | Yes | Pre-training Dataset. ONCE [14] is a large-scale dataset... Description of Downstream Datasets. 1) Waymo Open Dataset [20] contains 150k frames. 2) nu Scenes Dataset [1] provides point cloud data... 3) KITTI Dataset [5] includes 7481 training samples. |
| Dataset Splits | Yes | The labeled set divides into a train set with 6 sequences ( 5K samples), a validation set with 4 sequences ( 3k frames), and a test set with 10 sequences ( 8k frames)... Waymo Open Dataset [20]... divided into a train set with 798 sequences ( 150k samples) and a validation set with 202 sequences ( 40k samples)... nu Scenes Dataset [1]... consisting of 28130 training samples and 6019 validation samples. KITTI Dataset [5] includes 7481 training samples and is divided into a train set with 3712 samples and a validation set with 3769 samples. |
| Hardware Specification | Yes | We pre-train for 30 epochs on ONCE small split and large split using 8 NVIDIA Tesla A100 GPUs. |
| Software Dependencies | No | We use Adam optimizer with one-cycle learning rate schedule... Our code is based on 3DTrans [22]. The paper does not provide specific version numbers for software dependencies like PyTorch, Python, or the 3DTrans codebase. |
| Experiment Setup | Yes | We use Adam optimizer with one-cycle learning rate schedule and the maximum learning rate is set to 0.003. We pre-train for 30 epochs on ONCE small split and large split... The number of selected Ro I features is set to 256 and the cross-view matching threshold τ is set to 0.3. |