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

Abstract

In this paper, 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 2019 Contest addressed the problem of 3D reconstruction and 3D semantic understanding on a large scale. Several competitions were organized to assess specific issues, such as elevation estimation and semantic mapping from a single view, two views, or multiple views. In this Part A, we report the results of the best-performing approaches for semantic 3D reconstruction according to these various setups , while 3D point cloud semantic mapping is discussed in Part B [1].

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

“…For the contest, we provided training and test datasets for each challenge track, including approximately 20% of the US3D data. Details of the data provided for semantic 3-D reconstruction (Tracks 1, 2, and 3) are presented in Part A [21]. For Track 4, we provided airborne LiDAR point clouds with approximately 80 cm aggregate nominal pulse spacing.…”

Report on the 2021 IEEE GRSS Data Fusion Contest—Geospatial Artificial Intelligence for Social Good [Technical Committees]

“…The ultimate goal of the contest is to build models to understand the state and changes in the manmade and natural environment using multisensor and multitemporal remote sensing data for sustainable development. This contest was designed as a benchmarking competition following previous editions [1], [2], [4], [6], [7]. The 2021 DFC had two tracks running in parallel: 1) Track DSE: detection of settlements without electricity 2) Track MSD: multitemporal semantic change detection.…”

Single View Geocentric Pose in the Wild

“…Other features such as trees and buildings have known distributions of physically plausible heights. Kunwar [19] and Zheng et al [43] leveraged semantic cues as priors for height prediction to win the 2019 Data Fusion Contest (DFC19) singleview semantic 3D challenge track [20]. Srivistava et al [38] proposed to learn semantics and height jointly with a multi-task deep network.…”