Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation

• URL: http://arxiv.org/abs/2309.14932v1
• Date: Tue, 26 Sep 2023 13:41:30 GMT
• Title: Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation
• Authors: Wei Jong Yang, Guan Cheng Lee
• Abstract summary: We show that the projected positions of LiDAR points often misalign on the corresponding image. In this paper, we would like to address this problem carefully, with a specific focus on the nuScenes dataset and the SOTA of fusion models 2DPASS.
• Score: 0.391609470658968
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advent of advanced multi-sensor fusion models, there has been a notable enhancement in the performance of perception tasks within in terms of autonomous driving. Despite these advancements, the challenges persist, particularly in the fusion of data from cameras and LiDAR sensors. A critial concern is the accurate alignment of data from these disparate sensors. Our observations indicate that the projected positions of LiDAR points often misalign on the corresponding image. Furthermore, fusion models appear to struggle in accurately segmenting these misaligned points. In this paper, we would like to address this problem carefully, with a specific focus on the nuScenes dataset and the SOTA of fusion models 2DPASS, and providing the possible solutions or potential improvements.

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