  This work studies the semantic segmentation of 3D LiDAR data in dynamic
scenes for autonomous driving applications. A system of semantic segmentation
using 3D LiDAR data, including range image segmentation, sample generation,
inter-frame data association, track-level annotation and semi-supervised
learning, is developed. To reduce the considerable requirement of fine
annotations, a CNN-based classifier is trained by considering both supervised
samples with manually labeled object classes and pairwise constraints, where a
data sample is composed of a segment as the foreground and neighborhood points
as the background. A special loss function is designed to account for both
annotations and constraints, where the constraint data are encouraged to be
assigned to the same semantic class. A dataset containing 1838 frames of LiDAR
data, 39934 pairwise constraints and 57927 human annotations is developed. The
performance of the method is examined extensively. Qualitative and quantitative
experiments show that the combination of a few annotations and large amount of
constraint data significantly enhances the effectiveness and scene
adaptability, resulting in greater than 10% improvement
