The SIR2 Benchmark Dataset

We propose the Single Image Reflection Removal(SIR2) Benchmark Dataset with 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.).

Size of the data:
The overall size of the data is 186 MB

To ease the downloading, we separate the modalities of the samples into different files.
The size of each modality is shown in the below table:

Data Modality Size
Postcard Dataset 200 image triplets (600 images), 173,538 KB
Solid Object Dataset 200 image triplets (600 images), 13,520 KB
Wild Scene Dataset 100 image triplets (300 images), 4,154 KB
Total: 500 image triplets (1500 images), 186 MB

How to obtain the dataset:
Researchers can register an account, submite the request form and accept the Release Aggrement. We will validate your request and grand approve for downloading the datasets.

More info:
More information related with our dataset, and the latest published results on our dataset can be found here.

Sample Frames

Usage for Academic Reseach

Terms & Conditions of Use
The datasets are released for academic research only, and are free to researchers from educational or research institutes for non-commercial purposes.

The use of this dataset is governed by the following 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 any of these datasets are only allowed for the demonstration in academic publications and presentations.
• All users of these datasets 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.

If interested, researchers can register for an account, submit the request form and accept the Release Agreement. We will validate your request and grant approval for downloading the datasets.


Related Publications

All publications using the `SIR2' dataset should include the following acknowledgement: “(Portions of) the research in this paper used the `SIR2' 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, "Benchmarking Single-image Reflection Removal Algorithms", in International Conference on Computer Vision (ICCV), 2017 [PDF].