Cited by 4833D Shape Modeling and AnalysisAdvanced Neural Network ApplicationsAdvanced Vision and Imaging

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3D Graph Neural Networks for RGBD Semantic Segmentation

Qi, Xiaojuan, Liao, Renjie, Jia, Jiaya, Fidler, Sanja, Urtasun, Raquel·2017·2017 IEEE International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv.2017.556

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Abstract

RGBD semantic segmentation requires joint reasoning about 2D appearance and 3D geometric information. In this paper we propose a 3D graph neural network (3DGNN) that builds a k-nearest neighbor graph on top of 3D point cloud. Each node in the graph corresponds to a set of points and is associated with a hidden representation vector initialized with an appearance feature extracted by a unary CNN from 2D images. Relying on recurrent functions, every node dynamically updates its hidden representation based on the current status and incoming messages from its neighbors. This propagation model is unrolled for a certain number of time steps and the final per-node representation is used for predicting the semantic class of each pixel. We use back-propagation through time to train the model. Extensive experiments on NYUD2 and SUN-RGBD datasets demonstrate the effectiveness of our approach.

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Self-citation

Authors: Qi, X., Liao, R., Jia, J., Fidler, S., & Urtasun, R.
Affiliations: Chinese University of Hong Kong (HK)
University of Toronto (CA)
ORCID: Qi, Xiaojuan
Liao, Renjie
Jia, Jiaya
Fidler, Sanja
Urtasun, Raquel
Venue: 2017 IEEE International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2017-10
Pages: 5209-5218
DOI: 10.1109/iccv.2017.556
OpenAlex: W2776622059
Cited by: 482
References: 49
Citations by year: 2026: 9 · 2025: 28 · 2024: 39 · 2023: 52 · 2022: 62 · 2021: 122 · 2020: 71 · 2019: 68 · 2018: 30
FWCI: 70.80
Type: conference paper
Language: en
Open access: closed
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Funders: Natural Sciences and Engineering Research Council of Canada
Topics: Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
Keywords: point clouds, k-nearest neighbor graph, backpropagation through time, appearance features

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3D Graph Neural Networks for RGBD Semantic Segmentation

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