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UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering

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arxiv 2306.09117 v1 pith:U2JQRV47 submitted 2023-06-15 cs.CV cs.AI

Mingjie Pan , Li Liu , Jiaming Liu , Peixiang Huang , Longlong Wang , Shanghang Zhang , Shaoqing Xu , Zhiyi Lai
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Kuiyuan Yang
This is my paper · ORCID
classification cs.CVcs.AI
keywords occupancypredictionsemanticunioccchallengefine-grainedlabelsmethod
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In this technical report, we present our solution, named UniOCC, for the Vision-Centric 3D occupancy prediction track in the nuScenes Open Dataset Challenge at CVPR 2023. Existing methods for occupancy prediction primarily focus on optimizing projected features on 3D volume space using 3D occupancy labels. However, the generation process of these labels is complex and expensive (relying on 3D semantic annotations), and limited by voxel resolution, they cannot provide fine-grained spatial semantics. To address this limitation, we propose a novel Unifying Occupancy (UniOcc) prediction method, explicitly imposing spatial geometry constraint and complementing fine-grained semantic supervision through volume ray rendering. Our method significantly enhances model performance and demonstrates promising potential in reducing human annotation costs. Given the laborious nature of annotating 3D occupancy, we further introduce a Depth-aware Teacher Student (DTS) framework to enhance prediction accuracy using unlabeled data. Our solution achieves 51.27\% mIoU on the official leaderboard with single model, placing 3rd in this challenge.

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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. 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...

  2. SDGOCC: Semantic and Depth-Guided Bird's-Eye View Transformation for 3D Multimodal Occupancy Prediction

    cs.CV 2025-07 conditional novelty 6.0 of 10

    SDGOCC improves multimodal 3D occupancy prediction by using LiDAR depth and semantic masks to guide camera-to-BEV transformation, achieving state-of-the-art mIoU on Occ3D-nuScenes.

  3. TACOcc:Target-Adaptive Cross-Modal Fusion with Volume Rendering for 3D Semantic Occupancy

    cs.CV 2025-05 conditional novelty 6.0 of 10

    TACOcc reaches 28.4% mIoU on nuScenes 3D occupancy prediction by learning adaptive fusion neighborhoods and adding 3DGS-based photometric supervision.

  4. Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction

    cs.CV 2025-04 conditional novelty 6.0 of 10

    GDFusion fuses scene, motion, and geometry cues through gradient-descent-style RNN updates, improving mIoU by 1.4 to 4.8 points on Occ3D while cutting inference memory by 27 to 72 percent.

  5. AutoOcc: Automatic Open-Ended Semantic Occupancy Annotation via Vision-Language Guided Gaussian Splatting

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A camera-based pipeline that automatically produces open-ended 3D semantic occupancy labels via vision-language attention maps and Gaussian splatting, outperforming existing auto-labeling methods.

  6. Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting

    cs.CV 2024-11 conditional novelty 6.0 of 10

    EfficientOCF forecasts 3D occupancy by decoupling it into 2D BEV occupancy, height, and instance flow, achieving state-of-the-art accuracy and 82.33 ms inference on autonomous driving datasets.

  7. FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model

    cs.CV 2025-07 conditional novelty 5.0 of 10

    FMOcc uses flow matching with tri-perspective view and selective state space layers to improve 3D occupancy prediction from two camera frames.

  8. OcRFDet: Object-Centric Radiance Fields for Multi-View 3D Object Detection in Autonomous Driving

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Adding object-centric radiance-field rendering and height-aware opacity attention to the DualBEV detector improves camera-only 3D object detection on nuScenes by up to 2.0 mAP points.

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