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Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

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arxiv 2304.14365 v3 pith:RLNNQXMC submitted 2023-04-27 cs.CV

Xiaoyu Tian , Tao Jiang , Longfei Yun , Yucheng Mao , Huitong Yang , Yue Wang , Yilun Wang , Hang Zhao This is my paper · ORCID
classification cs.CV
keywords occupancybenchmarksdatasetocc3dpredictionpipelinescenesemantics
verification ladder T0 review T1 audit T2 compute T3 formal
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Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to overcome these limitations. To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene. This pipeline comprises three stages: voxel densification, occlusion reasoning, and image-guided voxel refinement. We establish two benchmarks, derived from the Waymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo and Occ3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of the proposed dataset with various baseline models. Lastly, we propose a new model, dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superior performance on the Occ3D benchmarks. The code, data, and benchmarks are released at https://tsinghua-mars-lab.github.io/Occ3D/.

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Forward citations

Cited by 8 Pith papers

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

  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. SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction

    cs.CV 2026-07 accept novelty 6.5 of 10

    SparseOcc++ decouples geometry completion (via orthogonal SCF regression on sparse anchors) from semantics, improving IoU 2.3 points and running 3.9 imes faster than SparseOcc on nuScenes while 5.9 imes faster than Oc...

  3. GaussianSeed: Hierarchical Gaussian Seeding for High-Resolution 3D Occupancy Prediction

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A hierarchical Gaussian occupancy representation with regression-based seeding predicts high-resolution 3D occupancy at lower latency than prior sparse baselines, validated on nuScenes and a new 0.1m campus dataset.

  4. GTAD: Global Temporal Aggregation Denoising Learning for 3D Semantic Occupancy Prediction

    cs.CV 2025-07 conditional novelty 5.0 of 10

    GTAD combines an in-model latent denoising network with global temporal interaction to improve camera-based 3D semantic occupancy prediction, reporting 40.76 mIoU on Occ3D-nuScenes at 12 epochs.

  5. QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction

    cs.CV 2025-06 conditional novelty 5.0 of 10

    QuadricFormer represents 3D scenes as a probabilistic mixture of superquadrics, improving accuracy and efficiency over Gaussian-based occupancy prediction on nuScenes.

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

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Diffusion-based generative models, using discrete categorical diffusion conditioned on BEV features, improve 3D occupancy prediction and downstream planning for autonomous driving.

  7. Generative AI for Autonomous Driving: A Review

    cs.CV 2025-05 conditional novelty 2.0 of 10

    A review of generative models (VAEs, GANs, diffusion, transformers, LLMs) applied to map generation, scenario generation, trajectory prediction, and motion planning for autonomous driving.

  8. Principles of Robot Autonomy

    cs.RO 2026-08 unverdicted novelty 1.0 of 10

    A comprehensive textbook framing autonomous robots through a See-Think-Act pipeline, with exercises and notebooks, but no new technical findings.

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