Perception-aware Multi-sensor Fusion for 3D LiDAR Semantic Segmentation

• URL: http://arxiv.org/abs/2106.15277v1
• Date: Mon, 21 Jun 2021 10:47:26 GMT
• Title: Perception-aware Multi-sensor Fusion for 3D LiDAR Semantic Segmentation
• Authors: Zhuangwei Zhuang, Rong Li, Yuanqing Li, Kui Jia, Qicheng Wang, Mingkui Tan
• Abstract summary: 3D semantic segmentation is important in scene understanding for many applications, such as auto-driving and robotics. Existing fusion-based methods may not achieve promising performance due to vast difference between 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.
• Score: 59.42262859654698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D LiDAR (light detection and ranging) based 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 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 great 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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