Related papers: Discwise Active Learning for LiDAR Semantic Segmentation

Discwise Active Learning for LiDAR Semantic Segmentation

URL: http://arxiv.org/abs/2309.13276v1
Date: Sat, 23 Sep 2023 06:08:22 GMT
Title: Discwise Active Learning for LiDAR Semantic Segmentation
Authors: Ozan Unal and Dengxin Dai and Ali Tamer Unal and Luc Van Gool
Abstract summary: Active learning (AL) provides a solution that can iteratively and intelligently label a dataset. We propose a discwise approach (DiAL) where in each iteration, we query the region a single frame covers on global coordinates, labeling all frames simultaneously.
Score: 92.92714645987083
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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