Release Agreement for SIR2 Dataset
SIR2 Benchmark Dataset consist of a large number and a great diversity of mixture images, and ground truth of background and reflection. Our dataset includes the
controlled scenes taken indoor and wild scenes taken outdoor. One part of the controlled scene is composed by a set of solid objects, which uses commonly available daily-life objects (e.g. ceramix mugs,
plush toys, fruits, etc.) for both the background and the reflected scenes. The other parts of the controlled scenes use five different postcards and combines them in a pair-wise manner by using each
card as background and reflection, respectively.
The wild scenes are with real-world objects of complex reflectance (car, tree leaves, glass windows, etc), various distances and scales (residential halls, gardens, and lecture room, etc), and different illuminations (direct sunlight, cloudy sky light and twilight, etc.).
The SIR2 Benchmark Dataset is the property of the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore.
Usage for Academic Reseach
The image database is released for academic research only, and is free to researchers from educational or research institutes for non-commercial purposes.
Terms and Conditions:
- Without the expressed permission of the ROSE Lab, any of the following will be considered illegal: redistribution, derivation or generation of a new dataset from this dataset, and commercial usage of any of these datasets in any way or form, either partially or in its entirety.
- For the sake of privacy, images of all subjects in this dataset are only allowed for the demonstration in academic publications and presentations.
- All users of the SIR2 Benchmark Dataset agree to indemnify, defend and hold harmless, the ROSE Lab and its officers, employees, and agents, individually and collectively, from any and all losses, expenses, and damages.
All publications using the SIR2 Benchmark dataset should include the following acknowledgement: "(Portions of) the research in this paper used the 'SIR2' Benchmark Dataset made
available by the ROSE Lab at the Nanyang Technological University, Singapore."
Furthermore, these publications should cite the following reference:
Renjie Wan, Boxin Shi, Ling-Yu Duan, Ah-Hwee Tan, and Alex C. Kot, "Benchamrking Single-image Reflection Removal Algorithms", in International Conferenceon Computer Vision (ICCV), 2017 [PDF] [bibtex].
By completing this online form, you have accepted the above terms & conditions and agree to include the above acknowledgements in your publications.