Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving
This addresses the problem of noisy and incomplete 3D scene understanding for autonomous driving systems, representing an incremental improvement over existing methods.
The paper tackles 3D occupancy prediction for autonomous driving by reframing it as a generative modeling task using diffusion models, resulting in outperforming state-of-the-art discriminative approaches with more realistic and accurate predictions, especially in occluded or low-visibility regions.
Accurately predicting 3D occupancy grids from visual inputs is critical for autonomous driving, but current discriminative methods struggle with noisy data, incomplete observations, and the complex structures inherent in 3D scenes. In this work, we reframe 3D occupancy prediction as a generative modeling task using diffusion models, which learn the underlying data distribution and incorporate 3D scene priors. This approach enhances prediction consistency, noise robustness, and better handles the intricacies of 3D spatial structures. Our extensive experiments show that diffusion-based generative models outperform state-of-the-art discriminative approaches, delivering more realistic and accurate occupancy predictions, especially in occluded or low-visibility regions. Moreover, the improved predictions significantly benefit downstream planning tasks, highlighting the practical advantages of our method for real-world autonomous driving applications.