Depth from Colonoscopy

This is a dataset we generated for our IPCAI 2019 submission "Implicit Domain Adaptation with Conditional Generative Adversarial Networks for Depth Prediction in Endoscopy". The data is currently being processed, and this is NOT a final version. Further details on the data generation process will follow here.

The dataset consists of 16,016 RGB images with corresponding ground truth. The images were resized to 256 x 256 pixels. We plan to release the original images at a later stage. The depth is scaled to [0,1] which corresponds to [0,20] cm. The data is divided into groups according to its texture (T1, T2, T3) and the lighting (L1, L2, L3). For each configuration there are four to five different subsets generated by randomly shifting and rotating the virtual camera. SRV

Enter password: click here to view and download the dataset

If you found our dataset helpful for your research please consider citing us:

title={Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy},
author={Rau, Anita and Edwards, PJ Eddie and Ahmad, Omer F and Riordan, Paul and Janatka, Mirek and Lovat, Laurence B and Stoyanov, Danail},
journal={International journal of computer assisted radiology and surgery},

Find our article here, return to our homepage, or see what the rest of our team is working on.

If you would like to download the dataset please contact Anita Rau [].

Creative Commons Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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