TY - JOUR
T1 - Computer vision for 3d perception a review
AU - O’Mahony, Niall
AU - Campbell, Sean
AU - Krpalkova, Lenka
AU - Riordan, Daniel
AU - Walsh, Joseph
AU - Murphy, Aidan
AU - Ryan, Conor
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - 3D computer vision
KW - Geometric deep learning
KW - Range imaging
UR - http://www.scopus.com/inward/record.url?scp=85060474201&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01057-7_59
DO - 10.1007/978-3-030-01057-7_59
M3 - Article
AN - SCOPUS:85060474201
SN - 2194-5357
VL - 869
SP - 788
EP - 804
JO - Advances in Intelligent Systems and Computing
JF - Advances in Intelligent Systems and Computing
ER -