3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving

• URL: http://arxiv.org/abs/2501.14605v1
• Date: Fri, 24 Jan 2025 16:22:35 GMT
• Title: 3DLabelProp: Geometric-Driven Domain Generalization for LiDAR Semantic Segmentation in Autonomous Driving
• Authors: Jules Sanchez, Jean-Emmanuel Deschaud, François Goulette,
• Abstract summary: Domain generalization aims to find ways for deep learning models to maintain their performance despite domain shifts between training and inference datasets. This is particularly important for models that need to be robust or are costly to train. This work proposes a geometry-based approach, leveraging the sequential structure of LiDAR sensors, which sets it apart from the learning-based methods commonly found in the literature.
• Score: 7.35996217853436
• License:
• Abstract: Domain generalization aims to find ways for deep learning models to maintain their performance despite significant domain shifts between training and inference datasets. This is particularly important for models that need to be robust or are costly to train. LiDAR perception in autonomous driving is impacted by both of these concerns, leading to the emergence of various approaches. This work addresses the challenge by proposing a geometry-based approach, leveraging the sequential structure of LiDAR sensors, which sets it apart from the learning-based methods commonly found in the literature. The proposed method, called 3DLabelProp, is applied on the task of LiDAR Semantic Segmentation (LSS). Through extensive experimentation on seven datasets, it is demonstrated to be a state-of-the-art approach, outperforming both naive and other domain generalization methods.

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