Computer Science > Computer Vision and Pattern Recognition
arXiv:2404.02742 (cs)
[Submitted on 3 Apr 2024]
Title:LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
View a PDF of the paper titled LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis, by Zehan Zheng and 4 other authors
View PDFHTML (experimental)Abstract:Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS), LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis. In consideration of the sparsity and large-scale characteristics, we design a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner. Furthermore, we introduce geometric constraints derived from point clouds to improve temporal consistency. For the realistic synthesis of LiDAR point clouds, we incorporate the global optimization of ray-drop probability to preserve cross-region patterns. Extensive experiments on KITTI-360 and NuScenes datasets demonstrate the superiority of our method in accomplishing geometry-aware and time-consistent dynamic reconstruction. Codes are available atthis https URL.
| Comments: | Accepted by CVPR 2024. Project Page:this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2404.02742 [cs.CV] |
| (orarXiv:2404.02742v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2404.02742 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis, by Zehan Zheng and 4 other authors
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