INF-PCA: Implicit Neural Field-Based Interactive Point Cloud Semantic Annotation

Point cloud semantic segmentation helps Intelligent Transportation Systems understand traffic scenes by assigning semantic label to each point in the point cloud, and it relies on large amounts of annotated training data. Nevertheless, manually annotating large-scale datasets of complex traffic scenes is quite time-consuming and tedious. This paper proposes INF-PCA, an interactive point cloud semantic annotation method based on implicit neural field, which allows users to achieve high-quality, large-scene and fast-response semantic annotation with only a few dozen mouse clicks. Firstly, the appearance, geometry and semantics of the point clouds are jointly represented by an implicit neural field, which maps a 3D spatial coordinate to its corresponding attributes. Secondly, an uncertainty-based semantic entropy loss and a supervoxel-based local consistency loss are designed to force the network to produce deterministic predictions with local consistency, thus generating smoother and more accurate boundaries. Furthermore, an active learning-based strategy for click-free annotation is proposed and analyzed to further reduce annotation pressure. Comprehensive experiments on multiple datasets including the road scene dataset Toronto3D revealed that INF-PCA can achieve more accurate annotations with faster response speed and only half of the clicks employed by the state-of-the-art methods, and that INF-PCA can be directly applied to intelligent transportation applications such as interactive segmentation of road scenes, inventory of transportation infrastructure assets, and production of high-definition map.