Enhancing 3D Semantic Scene Completion with a Refinement Module
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918517160280064 |
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| author | Zhang, Dunxing Lu, Jiachen Yang, Han Bao, Lei Song, Bo |
| author_facet | Zhang, Dunxing Lu, Jiachen Yang, Han Bao, Lei Song, Bo |
| contents | We propose ESSC-RM, a plug-and-play Enhancing framework for Semantic Scene Completion with a Refinement Module, which can be seamlessly integrated into existing SSC models. ESSC-RM operates in two phases: a baseline SSC network first produces a coarse voxel prediction, which is subsequently refined by a 3D U-Net-based Prediction Noise-Aware Module (PNAM) and Voxel-level Local Geometry Module (VLGM) under multiscale supervision. Experiments on SemanticKITTI show that ESSC-RM consistently improves semantic prediction performance. When integrated into CGFormer and MonoScene, the mean IoU increases from 16.87% to 17.27% and from 11.08% to 11.51%, respectively. These results demonstrate that ESSC-RM serves as a general refinement framework applicable to a wide range of SSC models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_18363 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing 3D Semantic Scene Completion with a Refinement Module Zhang, Dunxing Lu, Jiachen Yang, Han Bao, Lei Song, Bo Computer Vision and Pattern Recognition We propose ESSC-RM, a plug-and-play Enhancing framework for Semantic Scene Completion with a Refinement Module, which can be seamlessly integrated into existing SSC models. ESSC-RM operates in two phases: a baseline SSC network first produces a coarse voxel prediction, which is subsequently refined by a 3D U-Net-based Prediction Noise-Aware Module (PNAM) and Voxel-level Local Geometry Module (VLGM) under multiscale supervision. Experiments on SemanticKITTI show that ESSC-RM consistently improves semantic prediction performance. When integrated into CGFormer and MonoScene, the mean IoU increases from 16.87% to 17.27% and from 11.08% to 11.51%, respectively. These results demonstrate that ESSC-RM serves as a general refinement framework applicable to a wide range of SSC models. |
| title | Enhancing 3D Semantic Scene Completion with a Refinement Module |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.18363 |