Computer vision for 3d perception a review

Niall O’Mahony, Sean Campbell, Lenka Krpalkova, Daniel Riordan, Joseph Walsh, Aidan Murphy, Conor Ryan

Research output: Contribution to journalArticlepeer-review

Abstract

This paper will review the progress which has been made in Artificial Intelligence and Computer Vision particularly in 3D computer vision. There has been a lot of activity in the development of both hardware and software in 3D imaging systems which will have a huge impact in the capabilities of robotics. This paper reviews the latest advancements in the state of the art in range imaging sensors as well as some emerging technologies. For example, Time of Flight (ToF) cameras with improved resolution and latency, low cost LiDAR, and the fusion of range imaging technologies will empower robotics with greater perception capabilities. Likewise, software approaches will be reviewed with a focus on Deep Learning approaches which are now the leading edge in data analysis and further enhancing the capabilities of intelligent robotic systems using 3D imaging. The emergence of Geometric Deep Learning for 3D computer vision in robotics will also be detailed, with a focus on object registration, object detection and semantic segmentation. Foreseeable trends which have been identified in both hardware and software aspects of 3D computer vision are also discussed.

Original languageEnglish
Pages (from-to)788-804
Number of pages17
JournalAdvances in Intelligent Systems and Computing
Volume869
DOIs
Publication statusPublished - 2018

Keywords

  • 3D computer vision
  • Geometric deep learning
  • Range imaging

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