Reproduce “DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Kaiwen Cai, Xinze Liu, Xia Zhou, Hengtong Hu, Jie Xiang, Luyao Zhang, Xueyang Zhang, Kun Zhan and 2 more
DriveLiDAR4D enables the generation of sequential and controllable 4D LiDAR scenes for autonomous driving by integrating multimodal conditions with a specialized spatio-temporal diffusion model. This system shows enhanced spatial fidelity, temporal consistency, and achieves a 3D object detection mAP of 0.407 on synthetic data, representing a substantial improvement over prior generation methods on standard benchmarks like nuScenes.
Key insights
- Combining pixel-level road sketches, detailed scene captions, and pre-generated object priors provides fine-grained control over both static and dynamic elements in LiDAR scenes.
- Incorporating object priors effectively addresses the challenge of imbalanced point cloud density, improving the fidelity of generated foreground objects.
- Novel equirectangular spatio-temporal convolution and transformer modules are essential for ensuring both spatial coherence and temporal consistency in generated LiDAR sequences.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.