Cited by 216Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety3D Shape Modeling and Analysis

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Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation

Zhuang, Zhuangwei, Li, Rong, Jia, Kui, Wang, Qicheng, Li, Yuanqing, Tan, Mingkui·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.01597

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Abstract

3D LiDAR (light detection and ranging) semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinates to provide spatio-depth information for RGB images. Then, we propose a two-stream network to extract features from the two modalities, separately, and fuse the features by effective residual-based fusion modules. Moreover, we propose additional perception-aware losses to measure the perceptual difference between the two modalities. Extensive experiments on two benchmark data sets show the superiority of our method. For example, on nuScenes, our PMF outperforms the state-of-the-art method by 0.8% in mIoU.

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Authors: Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., & Tan, M.
Affiliations: South China University of Technology (CN)
Shenzhen Youjia Innov Tech Co., Ltd
ORCID: Zhuang, Zhuangwei
Li, Rong
Jia, Kui
Wang, Qicheng
Li, Yuanqing
Tan, Mingkui
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 16260-16270
DOI: 10.1109/iccv48922.2021.01597
OpenAlex: W3173732446
Cited by: 216
References: 65
Citations by year: 2026: 26 · 2025: 58 · 2024: 68 · 2023: 47 · 2022: 16 · 2021: 1
FWCI: 10.79
Type: conference paper
Language: en
Open access: closed
Licence: doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
Funders: Research and Development
Ministry of Science and Technology
Fundamental Research Funds for the Central Universities
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
Keywords: multi-sensor fusion, RGB-LiDAR fusion, point cloud projection, two-stream network, autonomous driving, nuScenes

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Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation

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