CluB: Cluster Meets BEV for LiDAR-Based 3D Object Detection
Authors: Yingjie Wang, Jiajun Deng, Yuenan Hou, Yao Li, Yu Zhang, Jianmin Ji, Wanli Ouyang, Yanyong Zhang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments are conducted on Waymo and nu Scenes datasets, and our Clu B achieves state-of-the-art performance on both benchmarks. 4 Experiments In this section, we first make comparisons with the state-of-the-art methods on Waymo and nu Scenes datasets. Then, we conduct ablation studies to examine the effect of each component of the proposed Clu B. |
| Researcher Affiliation | Academia | Yingjie Wang1,2 , Jiajun Deng3 , Yuenan Hou2, Yao Li1, Yu Zhang1, Jianmin Ji1, Wanli Ouyang2, Yanyong Zhang1 1University of Science and Technology of China, 2Shanghai AI Laboratory, 3The University of Adelaide, AIML |
| Pseudocode | No | The paper describes its method in detail but does not include a clearly labeled pseudocode block or algorithm. |
| Open Source Code | No | The paper states 'Our code is built on MMdetection3D [6]' but does not provide a specific link or explicit statement about the public release of their own implementation code for Clu B. |
| Open Datasets | Yes | Extensive experiments are conducted on Waymo and nu Scenes datasets, and our Clu B achieves state-of-the-art performance on both benchmarks. Waymo Open Dataset [26] Nu Scenes [2] dataset |
| Dataset Splits | Yes | For Waymo Open Dataset [26] (WOD), 798, 202 and 150 sequences are used for training, validation and testing, respectively. Nu Scenes [2] dataset has 1,000 driving scenes, where 700, 150, and 150 scenes are chosen for training, validation and testing, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or specific cloud instance types used for running its experiments. |
| Software Dependencies | No | The paper mentions building code on 'MMdetection3D [6]' but does not provide specific version numbers for this or any other software dependencies. |
| Experiment Setup | Yes | For Waymo, we follow previous voxel-based methods [22, 35, 23, 21] to use point cloud range of [ 75.2m, 75.2m] [ 75.2m, 75.2m] [ 2.0m, 4.0m] with voxel size [0.1m, 0.1m, 0.15m] in x, y, and z-axes respectively. For nu Scenes, we use point cloud range of [ 51.2m, 51.2m] [ 51.2m, 51.2m] [ 5.0m, 3.0m] with voxel size [0.1m, 0.1m, 0.2m] in x, y, and z-axes respectively. We adopt the same data augmentation setting as [35], including random flipping, global scaling, global rotation, and groundtruth (GT) sampling [33] for Waymo dataset. ... We use the one-cycle [25] learning rate schedule and Adam W [16] optimizer with the maximal learning rate 0.001. In addition to the 12-epoch schedule for ablation studies, we adopt a longer schedule (36 epochs) to obtain the best performance on the validation and test set. During the evaluation, we use the NMS Io U threshold of [0.7, 0.25, 0.25]. |