Towards Realistic Scene Generation with LiDAR Diffusion Models

Haoxi Ran, Vitor Guizilini, Yue Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 14738-14748

Diffusion models (DMs) excel in photo-realistic image synthesis but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating in the point space struggle to preserve the curve-like patterns and 3D geometry of LiDAR scenes which consumes much of their representation power. In this paper we propose LiDAR Diffusion Models (LiDMs) to generate LiDAR-realistic scenes from a latent space tailored to capture the realism of LiDAR scenes by incorporating geometric priors into the learning pipeline. Our method targets three major desiderata: pattern realism geometry realism and object realism. Specifically we introduce curve-wise compression to simulate real-world LiDAR patterns point-wise coordinate supervision to learn scene geometry and patch-wise encoding for a full 3D object context. With these three core designs our method achieves competitive performance on unconditional LiDAR generation in 64-beam scenario and state of the art on conditional LiDAR generation while maintaining high efficiency compared to point-based DMs (up to 107xfaster). Furthermore by compressing LiDAR scenes into a latent space we enable the controllability of DMs with various conditions such as semantic maps camera views and text prompts. Our code and pretrained weights are available at https://github.com/hancyran/LiDAR-Diffusion.