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Bridging Stereo Geometry and BEV Representation with Reliable Mutual Interaction for Semantic Scene Completion

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arxiv 2303.13959 v6 pith:6OL5FPBW submitted 2023-03-24 cs.CV

Bohan Li , Yasheng Sun , Zhujin Liang , Dalong Du , Zhuanghui Zhang , Xiaofeng Wang , Yunnan Wang , Xin Jin
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Wenjun Zeng
This is my paper · ORCID
classification cs.CV
keywords semanticscenestereocompletionreliablerepresentationdensegeometry
verification ladder T0 review T1 audit T2 compute T3 formal
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3D semantic scene completion (SSC) is an ill-posed perception task that requires inferring a dense 3D scene from limited observations. Previous camera-based methods struggle to predict accurate semantic scenes due to inherent geometric ambiguity and incomplete observations. In this paper, we resort to stereo matching technique and bird's-eye-view (BEV) representation learning to address such issues in SSC. Complementary to each other, stereo matching mitigates geometric ambiguity with epipolar constraint while BEV representation enhances the hallucination ability for invisible regions with global semantic context. However, due to the inherent representation gap between stereo geometry and BEV features, it is non-trivial to bridge them for dense prediction task of SSC. Therefore, we further develop a unified occupancy-based framework dubbed BRGScene, which effectively bridges these two representations with dense 3D volumes for reliable semantic scene completion. Specifically, we design a novel Mutual Interactive Ensemble (MIE) block for pixel-level reliable aggregation of stereo geometry and BEV features. Within the MIE block, a Bi-directional Reliable Interaction (BRI) module, enhanced with confidence re-weighting, is employed to encourage fine-grained interaction through mutual guidance. Besides, a Dual Volume Ensemble (DVE) module is introduced to facilitate complementary aggregation through channel-wise recalibration and multi-group voting. Our method outperforms all published camera-based methods on SemanticKITTI for semantic scene completion. Our code is available on https://github.com/Arlo0o/StereoScene.

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Cited by 8 Pith papers

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

  1. One Step Closer: Creating the Future to Boost Monocular Semantic Scene Completion

    cs.CV 2025-07 conditional novelty 7.0 of 10

    CF-SSC predicts pseudo-future frames from past and current monocular images and fuses them in 3D, achieving state-of-the-art semantic scene completion on SemanticKITTI and SSCBench-KITTI-360.

  2. Geospatial-Prior Guidance for 3D Semantic Scene Completion

    cs.CV 2026-08 conditional novelty 6.0 of 10

    GeoScene uses weighted fusion of satellite imagery and OpenStreetMap priors to improve camera-based 3D semantic scene completion on SemanticKITTI and SSCBench-KITTI-360.

  3. GEM-Occ: From Visual Geometry Evidence to Embodied Semantic Occupancy Memory

    cs.RO 2026-07 conditional novelty 6.0 of 10

    GEM-Occ converts transient visual geometry into semantic Gaussian and free-space ray evidence, fuses them into hierarchical occupancy memory, and beats prior indoor occupancy baselines on the new HIOcc benchmark.

  4. 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.

  5. Disentangling Instance and Scene Contexts for 3D Semantic Scene Completion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dual-stream BEV architecture that separates instance and scene class queries achieves state-of-the-art mIoU of 17.35 on SemanticKITTI and 20.55 on SSCBench-KITTI-360.

  6. VoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection

    cs.GR 2025-06 conditional novelty 6.0 of 10

    VoxDet reformulates 3D semantic occupancy prediction as dense object detection by deriving instance-boundary offsets from voxel class labels, and reports new state-of-the-art results on camera and LiDAR benchmarks.

  7. 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 ...

  8. Challenger: Affordable Adversarial Driving Video Generation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A framework for automatic generation of photorealistic adversarial driving videos, shown to sharply increase collision rates of end-to-end autonomous driving models.

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