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..
Exploring Active 3D Object Detection from a Generalization Perspective
Authors: Yadan Luo, Zhuoxiao Chen, Zijian Wang, Xin Yu, Zi Huang, Mahsa Baktashmotlagh
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate the effectiveness and applicability of CRB, we conduct extensive experiments on the two benchmark 3D object detection datasets of KITTI and Waymo and examine both one-stage (i.e., SECOND) and two-stage 3D detectors (i.e., PVRCNN). |
| Researcher Affiliation | Academia | Yadan Luo , Zhuoxiao Chen , Zijian Wang, Xin Yu, Zi Huang, Mahsa Baktashmotlagh The University of Queensland, Australia |
| Pseudocode | Yes | The algorithm is summarized in the supplemental material. |
| Open Source Code | Yes | Source code: https://github.com/Luoyadan/CRB-active-3Ddet. |
| Open Datasets | Yes | Datasets. KITTI (Geiger et al., 2012) is one of the most representative datasets for point cloud based object detection. The Waymo Open dataset (Sun et al., 2020) is a challenging testbed for autonomous driving, containing 158,361 training samples and 40,077 testing samples. |
| Dataset Splits | Yes | The dataset consists of 3,712 training samples (i.e., point clouds) and 3,769 val samples. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running experiments. |
| Software Dependencies | No | The paper mentions developing an 'active-3D-det toolbox' but does not specify any software dependencies with version numbers (e.g., Python version, PyTorch version, CUDA version). |
| Experiment Setup | Yes | The K1 and K2 are empirically set to 300 and 200 for KITTI and 2, 000 and 1, 200 for Waymo. We specify the settings of hyper-parameters, the training scheme and the implementation details of our model and AL baselines in Sec. B of the supplementary material. |