Humanoid-OmniOcc delivers a large-scale panoramic stereo occupancy dataset for humanoid robots via Real2Sim2Real, with a model that outperforms monocular baselines in both unseen sim scenes and real settings.
Occdepth: A depth-aware method for 3d semantic scene completion
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
UnsOcc proposes RenderFusion and GSRefinement to improve 3D semantic occupancy prediction in unstructured scenes by enhancing cross-modal fusion and long-tail supervision, outperforming SOTA on a new mine dataset and nuScenes.
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.
citing papers explorer
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Humanoid-OmniOcc: Stereo-Based Full-View Occupancy Dataset for Embodied AI
Humanoid-OmniOcc delivers a large-scale panoramic stereo occupancy dataset for humanoid robots via Real2Sim2Real, with a model that outperforms monocular baselines in both unseen sim scenes and real settings.
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UnsOcc: 3D Semantic Occupancy Prediction in Unstructured Scene via Rendering Fusion
UnsOcc proposes RenderFusion and GSRefinement to improve 3D semantic occupancy prediction in unstructured scenes by enhancing cross-modal fusion and long-tail supervision, outperforming SOTA on a new mine dataset and nuScenes.
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Sparsity-Aware Voxel Attention and Foreground Modulation for 3D Semantic Scene Completion
VoxSAMNet introduces sparsity-aware deformable attention via a dummy node and foreground modulation with dropout plus text-guided filtering to reach new state-of-the-art mIoU of 18.2% on SemanticKITTI and 20.2% on SSCBench-KITTI-360 for monocular 3D scene completion.