Monocular Depth Estimation Challenge
@DepthChallenge
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Monocular Depth Estimation Challenge Workshop at WACV 2023
Joined September 2022
This year we’ll be hosting some great keynote talks by @ftm_guney, @eric_brachmann & @vitorguizilini! The organization team also includes 3 new members: Ripudaman Arora, @mattpoggi & @fabiotosi92 👋 For more details and updates, please check https://t.co/jGMTqM75oH 2/2
jspenmar.github.io
MDEC @ CVPR 2025
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We’re very excited to announce that the 3rd edition of MDEC will be taking place at @CVPR in Seattle! The challenge, accepting both supervised and self-supervised approaches, will be running from 1st Feb - 25th Mar on CodaLab ( https://t.co/yV3kq33vWH) 1/2
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The 2nd edition of #MDEC at #CVPR2023 is right around the corner! Happening June 18th (08:30 - 12:00 PDT) * In person: West 208 - 209 * Online: Zoom link at https://t.co/QXot8e9O4O * Workshop details: https://t.co/jGMTqM7Def
@CVPR @cvssp_research
jspenmar.github.io
MDEC @ CVPR 2025
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Less than a month left until #CVPR2023! 🇨🇦 We've got some great keynotes by @oisinmacaodha, Daniel Cremers and @alexgkendall, discussing monocular depth, 3D reconstruction and foundation models! You can find more info at https://t.co/jGMTqM75oH
@cvssp_research @CVPR
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Thank you to all participants! We are looking forward to seeing you all at the workshop! Details can be found in https://t.co/jGMTqM75oH
@CVPR #CVPR2023 @cvssp_research 4/4
jspenmar.github.io
MDEC @ CVPR 2025
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Team imec-IDLab-UAntwerp instead relied on a ConvNeXt-v2-H encoder and an HR-Depth decoder (modified to use deformable convolutions). The network was trained using the min reconstruction photometric loss and edge-aware smoothness on the Kitti Eigen Benchmark dataset. 3/4
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Team DJI&ZJU used of a ConvNeXt-L + LeRes network, trained on 11 datasets. They made use of the SILog, pairwise normal regression, virtual normal and random proposal normalization losses. Ground-truth depths were rescaled an arbitrary focal length to improve convergence. 2/4
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The results and paper for the second edition of MDEC are out! You can find the paper at https://t.co/8PvdomvGTQ. Congratulations to the winning teams DJI&ZJU (supervised) and imec-IDLab-UAntwerp (self-supervised) 🎉 1/4
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The 2nd #MDEC is now over! Thanks to all participants. Results will be released in the coming weeks. We are also very excited to announce that the final workshop keynote will be given by Daniel Cremers! Stayed tuned for more details. #CVPR2023 @cvssp_research
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The recordings for the 1st edition of MDEC are now available at https://t.co/HAb8d4qJ47 You can also check them out in the thread below 👇
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Excited to announce that the 2nd Monocular Depth Estimation Challenge will be happening at #CVPR2023! Featuring: 🏆 New challenge accepting *any* type of supervision 🎙️Keynote talks by @alexgkendall and @oisinmacaodha For more details check
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Reminder that the Monocular Depth Estimation Challenge Workshop for #WACV2023 will be happening tomorrow (Sat 7th)! The workshop will be both in-person and virtual, starting at 08:20 HST (18:20 GMT). Please find the details for the workshop at
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MonoViT Based on the MonoViT architecture ( https://t.co/PSkf5e2AMy), consisting of convolutional and MPViT encoder blocks. Trained using the min photometric reconstruction loss and proxy depth maps from a pretrained self-supervised stereo network.
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z.suri The network used was ConvNeXt+DiffNet, trained with an additional stereo pose regression loss.
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OPDAI This submission featured a ConvNeXt+HRDepth architecture, trained with a wide variety of losses. This includes photometric reconstruction, autoencoder feature reconstruction, virtual stereo and proxy depth maps.
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