360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation

• URL: http://arxiv.org/abs/2309.06197v1
• Date: Tue, 12 Sep 2023 13:04:41 GMT
• Title: 360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation
• Authors: Laurenz Reichardt, Nikolas Ebert, Oliver Wasenm\"uller
• Abstract summary: Deep learning applications on LiDAR data suffer from a strong domain gap when applied to different sensors or tasks. In practical applications labeled data is costly and time consuming to obtain. We propose ImageTo360, an effective and streamlined few-shot approach to label-efficient LiDAR segmentation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning applications on LiDAR data suffer from a strong domain gap when applied to different sensors or tasks. In order for these methods to obtain similar accuracy on different data in comparison to values reported on public benchmarks, a large scale annotated dataset is necessary. However, in practical applications labeled data is costly and time consuming to obtain. Such factors have triggered various research in label-efficient methods, but a large gap remains to their fully-supervised counterparts. Thus, we propose ImageTo360, an effective and streamlined few-shot approach to label-efficient LiDAR segmentation. Our method utilizes an image teacher network to generate semantic predictions for LiDAR data within a single camera view. The teacher is used to pretrain the LiDAR segmentation student network, prior to optional fine-tuning on 360$^\circ$ data. Our method is implemented in a modular manner on the point level and as such is generalizable to different architectures. We improve over the current state-of-the-art results for label-efficient methods and even surpass some traditional fully-supervised segmentation networks.

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