Transformation-Equivariant 3D Object Detection for Autonomous Driving
Authors: Hai Wu, Chenglu Wen, Wei Li, Xin Li, Ruigang Yang, Cheng Wang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |