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OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments

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arxiv 2312.09243 v3 pith:6JOPKTEN submitted 2023-12-14 cs.CV

OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments

Chubin Zhang , Juncheng Yan , Yi Wei , Jiaxin Li , Li Liu , Yansong Tang , Yueqi Duan , Jiwen Lu This is my paper
classification cs.CV
keywords occupancypredictiondepthenvironmentsfieldsmethodoccnerfadopted
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Occupancy prediction reconstructs 3D structures of surrounding environments. It provides detailed information for autonomous driving planning and navigation. However, most existing methods heavily rely on the LiDAR point clouds to generate occupancy ground truth, which is not available in the vision-based system. In this paper, we propose an OccNeRF method for training occupancy networks without 3D supervision. Different from previous works which consider a bounded scene, we parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. The neural rendering is adopted to convert occupancy fields to multi-camera depth maps, supervised by multi-frame photometric consistency. Moreover, for semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model. Extensive experiments for both self-supervised depth estimation and 3D occupancy prediction tasks on nuScenes and SemanticKITTI datasets demonstrate the effectiveness of our method.

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Forward citations

Cited by 9 Pith papers

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

  1. FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction

    cs.CV 2026-07 conditional novelty 7.0

    Factorized Dense Routing approximates unconstrained 2D-to-3D feature mixing by hierarchical tensor contractions, yielding global-context occupancy prediction that remains robust without camera extrinsics.

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

    cs.CV 2026-06 unverdicted novelty 7.0

    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.

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

    cs.CV 2026-07 accept novelty 6.5

    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...

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

    cs.CV 2026-06 conditional novelty 6.0

    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.

  5. FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction

    cs.RO 2026-04 unverdicted novelty 6.0

    FreeOcc enables training-free open-vocabulary 3D occupancy prediction from RGB-D sequences by combining SLAM, dense Gaussian maps, off-the-shelf vision-language models, and probabilistic projection, achieving over 2x ...

  6. Monocular Open Vocabulary Occupancy Prediction for Indoor Scenes

    cs.CV 2026-02 unverdicted novelty 6.0

    A 3D Language-Embedded Gaussians framework with opacity-aware Poisson volumetric aggregation and progressive temperature decay achieves 59.50 IoU and 21.05 mIoU on Occ-ScanNet for open-vocabulary indoor occupancy.

  7. Semantic Causality-Aware Vision-Based 3D Occupancy Prediction

    cs.CV 2025-09 conditional novelty 6.0

    A class-conditional gradient loss (Causal Loss) plus channel-grouped lifting, learnable camera offsets, and normalized convolution raises Occ3D mIoU by 1.2/0.8 points and cuts the camera-noise mIoU drop from 32% to 7%.

  8. BePo: Dual Representation for 3D Occupancy Prediction

    cs.CV 2025-06 unverdicted novelty 6.0

    BePo proposes a dual BEV and sparse-points representation with cross-attention fusion for more accurate and efficient 3D occupancy prediction on autonomous driving benchmarks.

  9. VGOcc: Learning Visual-Geometric Gaussians for Vision-Centric 3D Driving Occupancy Prediction

    cs.CV 2026-07 conditional novelty 5.0

    VGOcc fuses frozen VGGT/DINOv2 features with sparse 3D Gaussians to reach 34.07 SC IoU and 21.75 SSC mIoU on nuScenes, besting prior vision-only occupancy methods.