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

• URL: http://arxiv.org/abs/2505.23115v2
• Date: Thu, 03 Jul 2025 06:55:33 GMT
• Title: Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving
• Authors: Yunshen Wang, Yicheng Liu, Tianyuan Yuan, Yingshi Liang, Xiuyu Yang, Honggang Zhang, Hang Zhao,
• Abstract summary: generative models learn the underlying data distribution and incorporate 3D scene priors.<n>Our experiments show that diffusion-based generative models outperform state-of-the-art discriminative approaches.
• Score: 27.94544631535978
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving [46.24100810736637]
  We introduce a self-supervised pre-training framework that learns effective 3D representations from scratch on unlabeled data.<n>The approach significantly improves model performance on downstream tasks such as 3D object detection, BEV segmentation, 3D object tracking, and occupancy prediction.
  arXiv  Detail & Related papers  (2025-04-17T07:26:11Z)
• An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training [50.71892161377806]
  DFIT-OccWorld is an efficient 3D occupancy world model that leverages decoupled dynamic flow and image-assisted training strategy.<n>Our model forecasts future dynamic voxels by warping existing observations using voxel flow, whereas static voxels are easily obtained through pose transformation.
  arXiv  Detail & Related papers  (2024-12-18T12:10:33Z)
• OccLoff: Learning Optimized Feature Fusion for 3D Occupancy Prediction [5.285847977231642]
  3D semantic occupancy prediction is crucial for ensuring the safety in autonomous driving. Existing fusion-based occupancy methods typically involve performing a 2D-to-3D view transformation on image features. We propose OccLoff, a framework that Learns to optimize Feature Fusion for 3D occupancy prediction.
  arXiv  Detail & Related papers  (2024-11-06T06:34:27Z)
• OPUS: Occupancy Prediction Using a Sparse Set [64.60854562502523]
  We present a framework to simultaneously predict occupied locations and classes using a set of learnable queries. OPUS incorporates a suite of non-trivial strategies to enhance model performance. Our lightest model achieves superior RayIoU on the Occ3D-nuScenes dataset at near 2x FPS, while our heaviest model surpasses previous best results by 6.1 RayIoU.
  arXiv  Detail & Related papers  (2024-09-14T07:44:22Z)
• AdaOcc: Adaptive-Resolution Occupancy Prediction [20.0994984349065]
  We introduce AdaOcc, a novel adaptive-resolution, multi-modal prediction approach. Our method integrates object-centric 3D reconstruction and holistic occupancy prediction within a single framework. In close-range scenarios, we surpass previous baselines by over 13% in IOU, and over 40% in Hausdorff distance.
  arXiv  Detail & Related papers  (2024-08-24T03:46:25Z)
• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• Volumetric Semantically Consistent 3D Panoptic Mapping [77.13446499924977]
  We introduce an online 2D-to-3D semantic instance mapping algorithm aimed at generating semantic 3D maps suitable for autonomous agents in unstructured environments. It introduces novel ways of integrating semantic prediction confidence during mapping, producing semantic and instance-consistent 3D regions. The proposed method achieves accuracy superior to the state of the art on public large-scale datasets, improving on a number of widely used metrics.
  arXiv  Detail & Related papers  (2023-09-26T08:03:10Z)
• DiffHPE: Robust, Coherent 3D Human Pose Lifting with Diffusion [54.0238087499699]
  We show that diffusion models enhance the accuracy, robustness, and coherence of human pose estimations. We introduce DiffHPE, a novel strategy for harnessing diffusion models in 3D-HPE. Our findings indicate that while standalone diffusion models provide commendable performance, their accuracy is even better in combination with supervised models.
  arXiv  Detail & Related papers  (2023-09-04T12:54:10Z)
• Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [63.3021778885906]
  3D bounding boxes are a widespread intermediate representation in many computer vision applications. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures. We release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications.
  arXiv  Detail & Related papers  (2022-10-13T23:57:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.