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..
Transformation-Equivariant 3D Object Detection for Autonomous Driving
Authors: Hai Wu, Chenglu Wen, Wei Li, Xin Li, Ruigang Yang, Cheng Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conducted comprehensive experiments on both KITTI (Geiger, Lenz, and Urtasun 2012) and Waymo dataset (Sun et al. 2020). |
| Researcher Affiliation | Collaboration | 1 School of Informatics, Xiamen University 2 Inceptio Technology 3 School of Performance, Visualization, and Fine Art, Texas A&M University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/hailanyi/TED. |
| Open Datasets | Yes | We conducted comprehensive experiments on both KITTI (Geiger, Lenz, and Urtasun 2012) and Waymo dataset (Sun et al. 2020). |
| Dataset Splits | Yes | For the KITTI dataset, we follow recent work (Deng et al. 2021b; Wu et al. 2022b) to divide the training data into a train split of 3712 frames and a val split of 3769 frames. |
| Hardware Specification | Yes | We train all the detectors on two 3090 GPU cards with a batch size of four and an Adam optimizer with a learning rate of 0.01. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We train all the detectors on two 3090 GPU cards with a batch size of four and an Adam optimizer with a learning rate of 0.01. For data augmentation, without rotation and reflection data augmentation, our method can achieve a high detection performance. With the data augmentation, we obtain slightly better results. Scaling, local augmentation and ground-truth sampling are also used. |