RGBD Datasets: Past, Present and Future
This is an incremental review paper that provides a structured overview for computer vision researchers working with RGBD data.
The paper reviews RGBD datasets across eight categories to help researchers find appropriate data and analyzes which datasets have successfully advanced computer vision, while identifying underexplored areas and suggesting future directions like synthetic data and dense reconstructions.
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.