DIML

We introduce an RGB-D scene dataset consisting of more than 200 indoor / outdoor scenes.

This dataset contains synchronized RGB-D frames from both Kinect v2 and Zed stereo camera. For the outdoor scene, we first generate disparity maps using an accurate stereo matching method and convert them using calibration parameters. A per-pixel confidence map of disparity is also provided. Our scenes are captured at various places, e.g., offices, rooms, dormitory, exhibition center, street, road etc., from Yonsei University and Ewha University

This dataset has been used to train convolutional neural networks in our project [1] and for our papers[2], [3], [4], [5]
"High quality 2D-to-multiview contents generation from large-scale RGB-D database", under Grant by the Institute for Information and Communications Technology Promotion(IITP) through the Korean Government(MSIP)(R0115-16-1007)

If you use this dataset in your research, please cite these publications :

1. DIML/CVL RGB-D technical_report : This material is presented to provide a detailed description about the DIML/CVl RGB-D dataset.
2. Y. Kim, B. Ham, C. Oh, and K. Sohn, "Structure selective depth super-resolution for RGB-D cameras," IEEE Trnas. on Image Processing, vol.25, no. 11, pp. 5527-38, Nov. 2016. [Paper]
3. S. Kim, D. Min, B. Ham, S. Kim, and K. Sohn, "Deep Stereo Confidence Prediction for Depth Estimation," IEEE International Conference on Image Processing, Sept. 2017. [Paper]
4. Y. Kim, H. Jung, D. Min, and K. Sohn, "Deep Monocular Depth Estimation via Integration of Global and Local Predictions," IEEE Trnas. on Image Processing, vol.27, no. 8, pp. 4131-43, Aug. 2018. [Paper]
5. J. Cho, D. Min, Y. Kim, and K. Sohn, "Deep Monocular Depth Estimation Leveraging a Large-scale Outdoor Stereo Dataset," Expert Systems with Applications, Mar. 2021. [Paper]