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Related Experiment Videos

Scaling Up Occupancy-centric Driving Scene Generation: Dataset and Method.

Bohan Li, Xin Jin, Hu Zhu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 22, 2026
    PubMed
    Summary
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    We introduce Nuplan-Occ, the largest semantic occupancy dataset, to advance autonomous driving scene generation. Our unified framework synthesizes occupancy, video, and LiDAR data, improving perception and planning tasks.

    Area of Science:

    • Computer Vision
    • Autonomous Driving Systems
    • Machine Learning for Robotics

    Background:

    • Occupancy-centric methods excel in autonomous driving scene generation but require extensive annotated data, which is scarce.
    • Existing datasets limit the scale and diversity needed for advanced generative modeling and downstream task evaluation.

    Purpose of the Study:

    • To address the scarcity of semantic occupancy data for autonomous driving.
    • To develop a unified framework for high-fidelity generation of semantic occupancy, multi-view videos, and LiDAR point clouds.
    • To enhance the performance of downstream autonomous driving applications like perception and planning.

    Main Methods:

    • Curated Nuplan-Occ, the largest semantic occupancy dataset from the Nuplan benchmark.
    • Developed a spatio-temporal disentangled architecture for 4D dynamic occupancy forecasting.

    Related Experiment Videos

  • Proposed Gaussian splatting-based rendering for video generation and sensor-aware embeddings for LiDAR simulation.

    • Main Results:

    • Achieved superior generation fidelity and scalability compared to existing methods.
    • Demonstrated practical value in downstream autonomous driving tasks.
    • Successfully synthesized high-quality semantic occupancy, multi-view videos, and LiDAR point clouds.

    Conclusions:

    • The Nuplan-Occ dataset and the proposed unified framework significantly advance generative modeling for autonomous driving.
    • The method provides a scalable solution for generating diverse and realistic driving scenes, crucial for robust system evaluation.