research-article

Supervoxel Convolution for Online 3D Semantic Segmentation

Authors:

Shi-Sheng Huang,

Shi-Min HuAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 40, Issue 3

Article No.: 34, Pages 1 - 15

https://doi.org/10.1145/3453485

Published: 01 August 2021 Publication History

Abstract

Online 3D semantic segmentation, which aims to perform real-time 3D scene reconstruction along with semantic segmentation, is an important but challenging topic. A key challenge is to strike a balance between efficiency and segmentation accuracy. There are very few deep-learning-based solutions to this problem, since the commonly used deep representations based on volumetric-grids or points do not provide efficient 3D representation and organization structure for online segmentation. Observing that on-surface supervoxels, i.e., clusters of on-surface voxels, provide a compact representation of 3D surfaces and brings efficient connectivity structure via supervoxel clustering, we explore a supervoxel-based deep learning solution for this task. To this end, we contribute a novel convolution operation (SVConv) directly on supervoxels. SVConv can efficiently fuse the multi-view 2D features and 3D features projected on supervoxels during the online 3D reconstruction, and leads to an effective supervoxel-based convolutional neural network, termed as Supervoxel-CNN, enabling 2D-3D joint learning for 3D semantic prediction. With the Supervoxel-CNN, we propose a clustering-then-prediction online 3D semantic segmentation approach. The extensive evaluations on the public 3D indoor scene datasets show that our approach significantly outperforms the existing online semantic segmentation systems in terms of efficiency or accuracy.

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Index Terms

Supervoxel Convolution for Online 3D Semantic Segmentation
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Embedded systems

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 40, Issue 3

June 2021

264 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3463476

Editor:
Marc Alexa
TU Berlin, Germany

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Publication History

Published: 01 August 2021

Accepted: 01 March 2021

Revised: 01 February 2021

Received: 01 October 2020

Published in TOG Volume 40, Issue 3

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Natural Science Foundation of China
Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology
Research Grants Council of the Hong Kong Special Administrative Region, China
City University of Hong Kong
China Postdoctoral Science Foundation

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Chen DLi ZZhou JFeng FDu TZhu B(2024)Solid-Fluid Interaction on Particle Flow MapsACM Transactions on Graphics10.1145/368795943:6(1-20)Online publication date: 19-Dec-2024
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Peng ZYang YShao TJiang CZhou K(2024)X-SLAM: Scalable Dense SLAM for Task-aware Optimization using CSFDACM Transactions on Graphics10.1145/365823343:4(1-15)Online publication date: 19-Jul-2024
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Mu TShen MLai YHu S(2024)Learning Virtual View Selection for 3D Scene Semantic SegmentationIEEE Transactions on Image Processing10.1109/TIP.2024.342195233(4159-4172)Online publication date: 2024
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