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NTIRE 2023: HR Depth from Images of Specular and Transparent Surfaces

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- We are delighted to inform you that the Booster dataset will be employed in the HR Depth from Images of Specular and Transparent Surfaces Challenge as a part of the NTIRE workshop in conjunction with CVPR 2023! -
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NEWS AND UPDATES

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  • 2023-06-05: Report paper on the challenge is out. Check it out! [Paper]
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  • 2023-06-05: Final Leaderboard Out! Check them out! [Leaderboard Mono][Leaderboard Stereo]
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  • 2023-03-04: Extended Challenge Paper Submission Deadlines!
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  • 2023-03-04: Extended Workshop and Challenge Deadlines!
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  • 2023-03-04: New Sponsorship! Prizes for each competiton winner! A big thank to Eyecan!
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  • 2023-02-07: Training and validation data has been released. Codalab servers online.
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  • 2022-12-16: Workshop proposal has been accepted.
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SPONSORS

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- Eyecan, an emerging AutoAi startup, focusing on deep learning for robotics without the need for either manual annotations or synthetic data, sponsored our challenges, granting a prize of 500$ to the competition winner (if we reach a minimum number of valid submissions). -
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INTRODUCTION

- Depth estimation has a long history in computer vision and has been intensively studied for decades. -
- Deep learning succeeds in this field as well, with modern deep networks achieving ludricous error rates on established datasets - such as KITTI, Middlebury, etc. -
- Should this evidence suggest that, thanks to deep learning, depth estimation is a solved problem? -
- Definitely, no! It is time for the community to focus on the open-challenges left unsolved in the field. - In particular, the Booster dataset identifies two of such hazards: -
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  • Non-Lambertian surfaces, such as those of transparent/reflectant objects
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  • High-resolution images
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- This challenge aims at fostering the community towards developing next-generation monocular or stereo depth networks capable of reasoning at an higher level, and thus yield accurate, - high-resolution 3D reconstructions for challenging objects, yet of common use. -
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CHALLENGE DESCRIPTION

- This challenge aims at estimating high-resolution disparity or depth maps from stereo or monocular images respectively. -
- The challenge will be divided into two phases: -
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  1. - Model Construction: - During this period, the partecipants will have to construct a model for the selected track (Monocular or Stereo). - The model can be trained using the Booster training split and any additional data. - The approach can be evaluated on the official validation set of each track. -
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  2. - Testing Period: - During this period, the participants can submite the predictions of their model on the official test set. - The disparity/depth maps will be evaluated by the organizers with the quantitative metrics. -
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TRACKS

- There will be two tracks for the challenge, both hosted by Codalab servers: - -
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DATASETS AND SUBMISSION

- All data can be downloaded from CodaLab. - We use CodaLab servers for online submission in the development and test phase, testing the results on the validation set and test set respectively. - After the test phase, the final results and the source codes (both training and test) need to be submitted via emails (boosterbenchmark@gmail.com). - Please refer to our online Codalab website for details of the submission rules [Codalab Stereo Track], [Codalab Mono Track]. -
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IMPORTANT DATES

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  • 2023-02-0: Release of training and validation data;
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  • 2023-02-07: Validation server online;
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  • 2023-03-07 Extended 2023-03-14: Final test data release, validation server closed;
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  • 2023-03-14 Extended 2023-03-20: Test result submission deadline;
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  • 2023-03-15 Extended 2023-03-21: Fact sheet / code / model submission deadline;
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  • 2023-03-17 Extended 2023-03-22: Test preliminary score release to the participants;
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  • 2023-03-28 Extended 2023-04-05: Challenge paper submission deadline;
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  • 2023-04-13 Camera ready paper submission deadline;
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REPORT PAPER

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