SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
This dataset addresses a critical gap for self-driving car research by providing extensive labeled LiDAR data, enabling development of advanced semantic segmentation methods, though it is incremental as it builds on existing KITTI data.
The authors tackled the lack of a large automotive LiDAR dataset for semantic scene understanding by introducing SemanticKITTI, a dataset with dense point-wise annotations from the KITTI Vision Odometry Benchmark, and proposed three benchmark tasks including semantic segmentation and scene completion, with baseline experiments showing the need for more sophisticated models.
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 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.