Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
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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PMF, using a ResNet34 backbone, achieves an mIoU of 63.9 on the KITTI dataset for 2D + 3D semantic segmentation.
“| PMF [107] | ResNet34 [16] | 63.9 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [107]
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Pseudo-LiDAR for Visual Odometry
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MRCNet: Multiresolution LiDAR-Camera Calibration Using Optical Center Distance Loss Network
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LiDAR in Connected and Autonomous Vehicles: Perception, Threat Model, and Defense
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A Cross-Modal Attention-Driven Multi-Sensor Fusion Method for Semantic Segmentation of Point Clouds
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DAPS3D: Domain Adaptive Projective Segmentation of 3D LiDAR Point Clouds
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LiDAR-Camera Continuous Fusion in Voxelized Grid for Semantic Scene Completion
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Small Object Detection on the Water Surface Based on Radar and Camera Fusion
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A Taxonomy of Semantic Information in Robot-Assisted Disaster Response
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Learning Modality-Agnostic Representation for Semantic Segmentation from Any Modalities
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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
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 16260-16270
- 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
- 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 › Engineering › Computational Mechanics › 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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