RGBD Datasets: Past, Present and Future
원문 초록
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.
함께 살펴볼 논문
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RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and Tracking
Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
LMPNet for Weakly-supervised Keypoint Discovery
Deterministic Object Pose Confidence Region Estimation
토론
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