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

2025-05-29

AI Summary

Discriminative methods for vision-based 3D occupancy prediction in autonomous driving exhibit limitations in robustness to sensor noise, consistency in occluded regions, and preservation of 3D structural integrity. Method: This paper introduces diffusion models—the first generative approach to 3D occupancy modeling—explicitly learning the underlying 3D scene data distribution and geometric priors to enhance robustness against incomplete observations and sensor noise. Our architecture employs a 3D convolutional encoder-decoder that fuses multi-view image features and generates voxel-wise occupancy probabilities via iterative denoising. Results: On benchmarks including nuScenes, our method significantly outperforms state-of-the-art discriminative approaches, particularly in occluded and low-visibility regions. Moreover, it improves downstream motion planning success rate by 12.7%, demonstrating superior generalization and geometric fidelity in real-world driving scenarios.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Predicting 3D occupancy grids accurately from visual inputs for autonomous driving
• Handling noisy data and incomplete observations in 3D scene structures
• Improving prediction consistency and robustness using diffusion-based generative models

Innovation

• Uses diffusion models for 3D occupancy prediction
• Enhances noise robustness and prediction consistency
• Improves accuracy in occluded or low-visibility areas