Enhancing 3D semantic scene completion with refinement module.

IF 3.5 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Frontiers in Neurorobotics Pub Date : 2026-03-06 eCollection Date: 2026-01-01 DOI:10.3389/fnbot.2026.1768219

Dunxing Zhang, Jiachen Lu, Han Yang, Lei Bao, Bo Song

引用次数: 0

Abstract

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 semantic scene completion (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.

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增强3D语义场景完成与细化模块。

我们提出了ESSC-RM，一个即插即用的语义场景完成增强框架，带有细化模块，可以无缝集成到现有的语义场景完成（SSC）模型中。ESSC-RM分为两个阶段：基线SSC网络首先产生粗体素预测，随后在多尺度监督下由基于3D u - net的预测噪声感知模块（PNAM）和体素级局部几何模块（VLGM）进行细化。SemanticKITTI上的实验表明，ESSC-RM持续提高语义预测性能。当集成到CGFormer和MonoScene中时，平均IoU分别从16.87增加到17.27%和从11.08增加到11.51%。这些结果表明，ESSC-RM作为一个通用的精化框架，适用于广泛的SSC模型。项目页面：https://github.com/LuckyMax0722/ESSC-RM和https://github.com/LuckyMax0722/VLGSSC。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS

CiteScore

5.20

自引率

6.50%

发文量

250

审稿时长

14 weeks

期刊介绍： Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.