# RGBD Datasets: Past, Present and Future

arXiv: https://arxiv.org/abs/1604.00999 · HTML: https://mlsanity.com/paper/1604.00999/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2016.

## Built on by (most cited first, 11 of 11)

- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [Single Image Depth Estimation: An Overview](https://mlsanity.com/paper/2104.06456/) (2021)
- [A Survey on Knowledge Editing of Neural Networks](https://mlsanity.com/paper/2310.19704/) (2023)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [RGBD Object Tracking: An In-depth Review](https://mlsanity.com/paper/2203.14134/) (2022)
- [TIMo -- A Dataset for Indoor Building Monitoring with a Time-of-Flight Camera](https://mlsanity.com/paper/2108.12196/) (2021)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition](https://mlsanity.com/paper/2205.01982/) (2022)
- [A metric for evaluating 3D reconstruction and mapping performance with no ground truthing](https://mlsanity.com/paper/2101.10402/) (2021)
- [BIDCD -- Bosch Industrial Depth Completion Dataset](https://mlsanity.com/paper/2108.04706/) (2021)
- [Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets](https://mlsanity.com/paper/2207.07016/) (2022)

## Abstract (arXiv)

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.