An End-to-End Point-Based Method and a New Dataset for Street-Level Point Cloud Change Detection.

Studies on 3-D change detection (3DCD) become a research hotspot with the development of 3-D sensors. However, most of the 3DCD works are focused on remote sensing data. In the field of street-level 3-D data, the related works are under investigation. The two main challenges are the lack of pointwise annotated datasets and a universal detection framework. In this article, we proposed an end-to-end point-based network named 3DCDNet and a new dataset named street-level point cloud change detection (SLPCCD) dataset to deliver street-level 3DCD task. To structure the proposed 3DCDNet, a local feature aggregation (LFA) module and a nearest feature difference (NFD) module are introduced. The LFA is capable of extracting point features and aggregating local information, which is an effective neural module for embedding point cloud features. By stacking multiple LFA blocks, the proposed network can be good at establishing relationships between different points and embedding semantically rich features. Different from the CD in images, another crucial point in 3DCD is how to identify changes since point clouds are unstructured data. In order to deliver this challenge, the NFD module is introduced to identify change results using the nearest query operation. Extensive experiments were implemented on the manually annotated SLPCCD dataset as well as another benchmark called Urb3DCD to validate the effectiveness and efficiency of the introduced network. It demonstrates that the proposed network outperforms popular existing approaches. The source code and the annotated dataset are available at: https://github.com/wangle53/3DCDNet.