Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest—Part B

Abstract

We present the scientific outcomes of the 2019 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest included challenges with large-scale datasets for semantic 3-D reconstruction from satellite images and also semantic 3-D point cloud classification from airborne LiDAR. 3-D reconstruction results are discussed separately in Part-A. In this Part-B, we report the results of the two best-performing approaches for 3-D point cloud classification. Both are deep learning methods that improve upon the PointSIFT model with mechanisms to combine multiscale features and task-specific postprocessing to refine model outputs.

Keywords

• Classification
• convolutional neural networks
• data fusion contest (DFC)
• deep learning
• image analysis and data fusion
• light detection and ranging (LiDAR)