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

Saket Kunwar, Hongyu Chen, Manhui Lin, Hongyan Zhang, Pablo D'Angelo, Daniele Cerra, Seyed Majid Azimi, Myron Brown, Gregory Hager, Naoto Yokoya, Ronny Hansch, Bertrand Le Saux

Research output: Contribution to journalArticlepeer-review

Abstract

In this article, 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 3-D reconstruction and 3-D 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 Part A, we report the results of the best-performing approaches for semantic 3-D reconstruction according to these various setups, whereas 3-D point cloud semantic mapping is discussed in Part B.

Original languageEnglish (US)
Article number9229514
Pages (from-to)922-935
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume14
DOIs
StatePublished - 2021

Keywords

  • 3-D reconstruction
  • Classification
  • Data Fusion Contest (DFC)
  • convolutional neural network (CNN)
  • deep learning
  • elevation model
  • height estimation
  • image analysis and data fusion (IADF)
  • light detection and ranging (LiDAR)
  • multiview
  • point cloud
  • semantic labeling
  • semantic mapping
  • stereo

ASJC Scopus subject areas

  • Computers in Earth Sciences
  • Atmospheric Science

Fingerprint Dive into the research topics of 'Large-Scale Semantic 3-D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest - Part A'. Together they form a unique fingerprint.

Cite this