LidarDM: Generative LiDAR Simulation in a Generated World
Vlas Zyrianov Henry Che Zhijian Liu Shenlong Wang
We present LidarDM, a novel LiDAR generative model capable of producing
realistic, layout-aware, physically plausible, and temporally coherent LiDAR
videos. LidarDM stands out with two unprecedented capabilities in LiDAR
generative modeling: (i) LiDAR generation guided by driving scenarios, offering
significant potential for autonomous driving simulations, and (ii) 4D LiDAR
point cloud generation, enabling the creation of realistic and temporally
coherent sequences. At the heart of our model is a novel integrated 4D world
generation framework. Specifically, we employ latent diffusion models to
generate the 3D scene, combine it with dynamic actors to form the underlying 4D
world, and subsequently produce realistic sensory observations within this
virtual environment. Our experiments indicate that our approach outperforms
competing algorithms in realism, temporal coherency, and layout consistency. We
additionally show that LidarDM can be used as a generative world model
simulator for training and testing perception models.