Discwise Active Learning for LiDAR Semantic Segmentation
Ozan Unal Dengxin Dai Ali Tamer Unal Luc Van Gool
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.