Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving

TL;DR

This paper introduces a diffusion-based generative modeling approach for 3D occupancy prediction in autonomous driving, improving robustness and accuracy over traditional discriminative methods, especially in challenging scenarios.

Contribution

It reformulates 3D occupancy prediction as a generative task using diffusion models, incorporating scene priors to enhance prediction quality and robustness.

Findings

Outperforms state-of-the-art discriminative methods in accuracy

Provides more realistic occupancy predictions in occluded regions

Enhances downstream planning tasks with better 3D scene understanding

Abstract

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.