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 excel in photo-realistic image synthesis%2C 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%2C which consumes much of their representation power. In this paper%2C 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%2C geometry realism%2C and object realism. Specifically%2C we introduce curve-wise compression to simulate real-world LiDAR patterns%2C point-wise coordinate supervision to learn scene geometry%2C and patch-wise encoding for a full 3D object context. With these three core designs%2C our method achieves competitive performance on unconditional LiDAR generation in 64-beam scenario and state of the art on conditional LiDAR generation%2C while maintaining high efficiency compared to point-based DMs (up to 107%24times%24 faster). Furthermore%2C by compressing LiDAR scenes into a latent space%2C we enable the controllability of DMs with various conditions such as semantic maps%2C camera views%2C and text prompts.?quality=80&w=800)
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