欢迎对基于物理的计算机视觉问题感兴趣的老师和同学关注：

https://www.springer.com/journal/11263/updates/19561744

We believe when physics based vision meets deep learning both low level and high level vision task can get the benefits. Furthermore, we believe that there are many computer vision tasks that can be tackled by solving both physics based vision and high level vision in a joint fashion to get more robust and accurate results which cannot be achieved by ignoring each side.

The topics of this special issue include, but are not limited to: Deep learning +

  • Photometric based 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
  • Neuromorphic (event/spike) cameras
  • Differential render and its applications

Important Dates

  • Submission deadline: extended to January 31, 2022
  • First review notification (tentative): March 30, 2022
  • Revision due: May 30, 2022
  • Final decision: June 30, 2022

Submission guidelines:

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guide lines available on the IJCV website at: https://www.springer.com/11263 .

Please select "Physics-Based Vision meets Deep Learning" at the beginning of the submission process.

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