Abstract
Semantic scene understanding is important for various applications. In particular, self-driving cars need a fine-grained understanding of the surfaces and objects in their vicinity. Light detection and ranging (LiDAR) provides precise geometric information about the environment and is thus a part of the sensor suites of almost all self-driving cars. Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR. In this paper, we introduce a large dataset to propel research on laser-based semantic segmentation. We annotated all sequences of the KITTI Vision Odometry Benchmark and provide dense point-wise annotations for the complete $360^{o}$ field-of-view of the employed automotive LiDAR. We propose three benchmark tasks based on this dataset: (i) semantic segmentation of point clouds using a single scan, (ii) semantic segmentation using sequences comprised of multiple past scans, and (iii) semantic scene completion, which requires to anticipate the semantic scene in the future. We provide baseline experiments and show that there is a need for more sophisticated models to efficiently tackle these tasks. Our dataset opens the door for the development of more advanced methods, but also provides plentiful data to investigate new research directions.
Abstract (translated)
语义场景理解对于各种应用程序都很重要。特别是，自动驾驶汽车需要对其附近的表面和物体有一个细致的了解。光探测和测距（LiDAR）提供了有关环境的精确几何信息，因此是几乎所有自动驾驶汽车的传感器套件的一部分。尽管语义场景理解与此应用程序相关，但此任务缺少基于汽车激光雷达的大型数据集。
URL
https://arxiv.org/abs/1904.01416
