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OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving

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arxiv 2311.16038 v1 pith:PAVWWJEY submitted 2023-11-27 cs.CV cs.AIcs.LG

Wenzhao Zheng , Weiliang Chen , Yuanhui Huang , Borui Zhang , Yueqi Duan , Jiwen Lu This is my paper
classification cs.CVcs.AIcs.LG
keywords occupancysceneoccworldmodelworlddrivingevolutionscenes
verification ladder T0 review T1 audit T2 compute T3 formal
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Understanding how the 3D scene evolves is vital for making decisions in autonomous driving. Most existing methods achieve this by predicting the movements of object boxes, which cannot capture more fine-grained scene information. In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes. We propose to learn a world model based on 3D occupancy rather than 3D bounding boxes and segmentation maps for three reasons: 1) expressiveness. 3D occupancy can describe the more fine-grained 3D structure of the scene; 2) efficiency. 3D occupancy is more economical to obtain (e.g., from sparse LiDAR points). 3) versatility. 3D occupancy can adapt to both vision and LiDAR. To facilitate the modeling of the world evolution, we learn a reconstruction-based scene tokenizer on the 3D occupancy to obtain discrete scene tokens to describe the surrounding scenes. We then adopt a GPT-like spatial-temporal generative transformer to generate subsequent scene and ego tokens to decode the future occupancy and ego trajectory. Extensive experiments on the widely used nuScenes benchmark demonstrate the ability of OccWorld to effectively model the evolution of the driving scenes. OccWorld also produces competitive planning results without using instance and map supervision. Code: https://github.com/wzzheng/OccWorld.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    VISA improves closed-set 3D occupancy mIoU on nuScenes by using VLM instance audits as reliability-weighted semantic supervisors during training of existing world models.

  2. Auto-JEPA: A Latent World Model of Continuous Intent for End-to-End Autonomous Driving

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A driving planner that predicts a future-ego-trajectory latent and retrieves executable trajectories from a fixed memory reaches 91.3 PDMS on NAVSIM v1 without reconstructing the scene.

  3. VISA: VLM-Guided Instance Semantic Auditing for 3D Occupancy World Models

    cs.CV 2026-06 conditional novelty 6.0 of 10

    Offline VLM instance audits, grounded to matched object voxels and distilled via taxonomy, attribute, and graph losses, raise OccWorld and GaussianWorld closed-set occupancy mIoU without inference-time VLM cost.

  4. AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond

    cs.RO 2026-05 unverdicted novelty 6.0 of 10

    AnyScene is an occupancy-centric framework using a Spatial-Temporal Occupancy Diffusion Transformer and Geometry-Grounded View Expansion to generate controllable driving scenes and videos from BEV layouts.

  5. Human Cognition in Machines: A Unified Perspective of World Models

    cs.RO 2026-04 unverdicted novelty 6.0 of 10

    The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and pro...

  6. OmniNWM: Omniscient Driving Navigation World Models

    cs.CV 2025-10 conditional novelty 6.0 of 10

    OmniNWM jointly generates long panoramic multi-modal driving videos, controls them precisely via normalized Plücker ray-maps, and derives dense driving rewards from generated 3D occupancy.

  7. Enhancing End-to-End Autonomous Driving with Latent World Model

    cs.CV 2024-06 accept novelty 6.0 of 10

    LAW introduces a self-supervised prediction task on latent scene features that boosts end-to-end driving performance on nuScenes, NAVSIM, and CARLA benchmarks.

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

    cs.CV 2025-10 conditional novelty 5.0 of 10

    UniScenev2 scales occupancy-centric driving-scene generation to NuPlan scale, releasing a 3.6M-frame semantic-occupancy dataset and jointly generating occupancy, video, and LiDAR that beats published baselines on its ...

  9. World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications

    cs.LG 2026-05 unverdicted novelty 3.0 of 10

    The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sor...

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