DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection
Authors: Xiang Li, Junbo Yin, Wei Li, Chengzhong Xu, Ruigang Yang, Jianbing Shen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the challenging DAIR-V2X and V2XSet benchmark datasets demonstrate DI-V2X achieves remarkable performance, outperforming all the previous V2X models. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Beijing Institute of Technology 2Inceptio 3SKL-IOTSC, CIS, University of Macau |
| Pseudocode | No | The paper includes architectural diagrams and flowcharts but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Serenos/DI-V2X. |
| Open Datasets | Yes | We employ the challenging DAIR-V2X (Yu et al. 2022) for evaluating our model and other SOTA approaches. We also evaluate our method on another V2X calibration dataset V2XSet (Xu et al. 2022a) |
| Dataset Splits | Yes | V2XSet (Xu et al. 2022a), which contains 6694 training data and 1920 validation data generated by the simulator. Extensive experiments on the challenging DAIR-V2X and V2XSet benchmark datasets demonstrate DI-V2X achieves remarkable performance, outperforming all the previous V2X models. Table 1: Comparison with state-of-the-art methods on DAIR-V2X val dataset. |
| Hardware Specification | Yes | All student models are trained on 4 NVIDIA Tesla V100 GPUs with a batch size of 4 for 40 epochs. |
| Software Dependencies | No | The paper mentions using 'Point Pillars' as a detector but does not specify version numbers for any software dependencies or libraries (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | We set the point cloud range to [ 100, 100] [ 40, 40] [ 3.5, 1.5] meters defined in the vehicle coordinate system with the voxel size as [0.4, 0.4, 5] meters along XY Z axes. All student models are trained on 4 NVIDIA Tesla V100 GPUs with a batch size of 4 for 40 epochs. We set λkd as 1 and the thresholds τl, τh as 0.2 and 0.8. |