LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis

Zehan Zheng, Fan Lu, Weiyi Xue, Guang Chen, Changjun Jiang
(† Corresponding author)
ISPC Lab, Tongji University

CVPR 2024 (Seattle, USA)

Demo Video


Dynamic reconstruction for LiDAR point clouds


Simulate different LiDAR configuration


Densitify LiDAR beams


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.




For large-scale autonomous driving scenarios, we utilize the 4D hybrid representation, which combines low-resolution multi-planar features and high-resolution hash grid features to achieve effective reconstruction. Then, multi-level spatio-temporal features aggregated by flow MLP are fed into neural LiDAR fields for density, intensity and ray-drop probability prediction. Finally, novel space-time view LiDAR point clouds are synthesized via differentiable rendering. Furthermore, we construct geometric constraints derived from point clouds for temporal consistency and the global optimization of ray-drop for generation realism.


Results (Depth)

Results (Intensity)


        title     = {LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis},
        author    = {Zheng, Zehan and Lu, Fan and Xue, Weiyi and Chen, Guang and Jiang, Changjun},
        booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
        year      = {2024}