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OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries

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arxiv 2312.03774 v3 pith:VM5GRZDN submitted 2023-12-06 cs.CV

Yuhang Lu , Xinge Zhu , Tai Wang , Yuexin Ma This is my paper · ORCID
classification cs.CV
keywords occupancyoctreepredictionoctreeocccomputationalinformationmethodssemantic
verification ladder T0 review T1 audit T2 compute T3 formal

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Occupancy prediction has increasingly garnered attention in recent years for its fine-grained understanding of 3D scenes. Traditional approaches typically rely on dense, regular grid representations, which often leads to excessive computational demands and a loss of spatial details for small objects. This paper introduces OctreeOcc, an innovative 3D occupancy prediction framework that leverages the octree representation to adaptively capture valuable information in 3D, offering variable granularity to accommodate object shapes and semantic regions of varying sizes and complexities. In particular, we incorporate image semantic information to improve the accuracy of initial octree structures and design an effective rectification mechanism to refine the octree structure iteratively. Our extensive evaluations show that OctreeOcc not only surpasses state-of-the-art methods in occupancy prediction, but also achieves a 15%-24% reduction in computational overhead compared to dense-grid-based methods.

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Cited by 6 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. Semantic Causality-Aware Vision-Based 3D Occupancy Prediction

    cs.CV 2025-09 conditional novelty 6.0 of 10

    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%.

  3. LightOcc: Lightweight Spatial Embedding for Efficient Vision-based 3D Occupancy Prediction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    LightOcc uses a one-channel occupancy volume, rearranged into tri-perspective views, to add height information to BEV features, reaching 47.24 mIoU on Occ3D-nuScenes with 8 history frames.

  4. GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    GaussianFormer-2 predicts 3D semantic occupancy from cameras by multiplying Gaussian occupancy probabilities and using a Gaussian mixture for semantics, beating prior methods with far fewer Gaussians.

  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. GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Prediction

    cs.CV 2024-12 conditional novelty 5.0 of 10

    A world model operating on 3D Gaussians forecasts the current occupancy from the previous frame and current RGB, improving mIoU by about 2 points on nuScenes without meaningful added latency.

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