M. Firman. (2016)cite arxiv:1604.00999Comment: 8 pages excluding references (CVPR style).
Abstract
Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.
Description
[1604.00999] RGBD Datasets: Past, Present and Future
%0 Generic
%1 firman2016datasets
%A Firman, Michael
%D 2016
%K 2016 arxiv computer-vision cvpr dataset paper
%T RGBD Datasets: Past, Present and Future
%U http://arxiv.org/abs/1604.00999
%X Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.
@misc{firman2016datasets,
abstract = {Since the launch of the Microsoft Kinect, scores of RGBD datasets have been
released. These have propelled advances in areas from reconstruction to gesture
recognition. In this paper we explore the field, reviewing datasets across
eight categories: semantics, object pose estimation, camera tracking, scene
reconstruction, object tracking, human actions, faces and identification. By
extracting relevant information in each category we help researchers to find
appropriate data for their needs, and we consider which datasets have succeeded
in driving computer vision forward and why.
Finally, we examine the future of RGBD datasets. We identify key areas which
are currently underexplored, and suggest that future directions may include
synthetic data and dense reconstructions of static and dynamic scenes.},
added-at = {2018-07-24T19:33:55.000+0200},
author = {Firman, Michael},
biburl = {https://www.bibsonomy.org/bibtex/259e15b31b09a3286d1af799f73a1ab90/analyst},
description = {[1604.00999] RGBD Datasets: Past, Present and Future},
interhash = {3767af6b07a36dfd89c0167e6932b218},
intrahash = {59e15b31b09a3286d1af799f73a1ab90},
keywords = {2016 arxiv computer-vision cvpr dataset paper},
note = {cite arxiv:1604.00999Comment: 8 pages excluding references (CVPR style)},
timestamp = {2018-07-24T19:33:55.000+0200},
title = {RGBD Datasets: Past, Present and Future},
url = {http://arxiv.org/abs/1604.00999},
year = 2016
}