Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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]. |