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Date
2023-11Type
- Journal Article
ETH Bibliography
yes
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Abstract
While LiDAR data acquisition is easy, labeling for semantic segmentation remains highly time consuming and must therefore be done selectively. Active learning (AL) provides a solution that can iteratively and intelligently label a dataset while retaining high performance and a low budget. In this work we explore AL for LiDAR semantic segmentation. As a human expert is a component of the pipeline, a practical framework must consider common labeling techniques such as sequential labeling that drastically improve annotation times. We therefore propose a discwise approach (DiAL), where in each iteration, we query the region a single frame covers on global coordinates, labeling all frames simultaneously. We then tackle the two major challenges that emerge with discwise AL. Firstly, we devise a new acquisition function that takes 3D point density changes into consideration which arise due to location changes or ego-vehicle motion. Next, we solve a mixed-integer linear program that provides a general solution to the selection of multiple frames while taking into consideration the possibilities of disc intersections. Finally we propose a semi-supervised learning approach to utilize all frames within our dataset and improve performance. Show more
Publication status
publishedExternal links
Journal / series
IEEE Robotics and Automation LettersVolume
Pages / Article No.
Publisher
IEEESubject
Autonomous agents; object detection; segmentation and categorization; semantic scene understandingOrganisational unit
03514 - Van Gool, Luc / Van Gool, Luc
Related publications and datasets
Is referenced by: https://doi.org/10.3929/ethz-b-000668173
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ETH Bibliography
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