[visionlist] C4P: PBDL 2021 : 3rd ICCV Workshop and Challenge on Physics Based Vision meets Deep Learning

[Shaodi YOU]

Following the success of 2nd ICCV Workshop on Physics Based Vision Meets
Deep Learning (PBDL2019). We propose the 3rd workshop using the same title
and topics with ICCV 2021. The goal is to encourage the interplay between
physics based vision and deep learning. Physics based vision aims to invert
the processes to recover the scene properties, such as shape, reflectance,
light distribution, medium properties, etc., from images. In recent years,
deep learning shows promising improvement for various vision tasks. When
physics based vision meets deep learning, there must be mutual benefits.


We welcome submissions of new methods in the classic physics based vision
problems, but preference will be given to novel insights inspired by
utilizing deep learning techniques. Relevant topics include but are not
limited to

Deep learning +
• Photometric 3D reconstruction
• Radiometric modeling/calibration of cameras
• Color constancy
• Illumination analysis and estimation
• Reflectance modeling, fitting, and analysis
• Forward/inverse renderings
• Material recognition and classification
• Transparency and multi-layer imaging
• Reflection removal
• Intrinsic image decomposition
• Light field imaging
• Multispectral/hyperspectral capture, modeling and analysis
• Vision in bad weather (dehaze, derain, etc.)
• Structured light techniques (sensors, BRDF measurement and analysis)
• TOF sensors and its applications

Paper submission is through CMT:
https://cmt3.research.microsoft.com/pbdl2021

Challenge:
https://pbdl2019.github.io/challenge/index.html

The format for paper submission is the same as the ICCV 2021 submission
format. Papers that violates the anonymity, do not use the ICCV submission
template or have more than 8 pages (excluding references) will be rejected
without review. The accepted papers will appear in the proceedings of ICCV
2021 workshops. In submitting a manuscript to this workshop, the authors
acknowledge that no paper substantially similar in content has been
submitted to another workshop or conference during the review period.



-- 
YOU, Shaodi
youshaodi at gmail.com
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