ORIGINAL RESEARCH article
Front. Neurorobot.
Enhancing 3D Semantic Scene Completion with Refinement Module
Provisionally accepted
- 1Technical University of Munich, Munich, Germany
- 2Tianjin University, Tianjin, China
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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. Project page: https://github.com/LuckyMax0722/ESSC-RM and https: //github.com/LuckyMax0722/VLGSSC.
Keywords: Plug & play, PNAM, refinement, Semantic scene completion, vison-language guidance
Received: 15 Dec 2025; Accepted: 29 Jan 2026.
Copyright: © 2026 Zhang, Lu, Yang, Bao and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Han Yang
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