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

Yunshen Wang, Yicheng Liu, Tianyuan Yuan, Yucheng Mao, Yingshi Liang, YangXiuyu, Honggang Zhang, Hang Zhao

Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving: 2 upvotes on Hugging Face Daily Papers, #37 of 46 papers on 2025-06-06. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.