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..
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 | Venue PDF | 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. |