论文标题
涂鸦监督的LIDAR语义分段
Scribble-Supervised LiDAR Semantic Segmentation
论文作者
论文摘要
密集的注释LiDAR点云仍然太昂贵且耗时，无法跟上越来越多的数据。尽管当前的文献专注于完全监督的绩效，但开发利用现实弱监督的有效方法尚未探索。在本文中，我们建议使用涂鸦注释LiDar Point Clouds并发布ScribbleKitti，这是LIDAR语义分割的第一个涂鸦宣布的数据集。此外，我们提出了一条管道，以减少使用如此弱注释时产生的性能差距。我们的管道包括三个独立贡献，它们可以与任何LiDAR语义分段模型结合使用，以实现多达95.7％的全面监督性能，同时仅使用8％的标记点。我们的涂鸦注释和代码可在github.com/ouenal/scribblekitti上找到。
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data. While current literature focuses on fully-supervised performance, developing efficient methods that take advantage of realistic weak supervision have yet to be explored. In this paper, we propose using scribbles to annotate LiDAR point clouds and release ScribbleKITTI, the first scribble-annotated dataset for LiDAR semantic segmentation. Furthermore, we present a pipeline to reduce the performance gap that arises when using such weak annotations. Our pipeline comprises of three stand-alone contributions that can be combined with any LiDAR semantic segmentation model to achieve up to 95.7% of the fully-supervised performance while using only 8% labeled points. Our scribble annotations and code are available at github.com/ouenal/scribblekitti.
