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Photography Basics : Spectral Sensitivity Estimation Without a Camera
https://color-lab-eilat.github.io/Spectral-sensitivity-estimation-web/
A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect methods that aim to lower cost and complexity by using color targets. However, the use of color targets gives rise to new complications that make the estimation more difficult, and consequently, there currently exists no simple, low-cost, robust go-to method for spectral sensitivity estimation that non-specialized research labs can adopt. Furthermore, even if not limited by hardware or cost, researchers frequently work with imagery from multiple cameras that they do not have in their possession.
To provide a practical solution to this problem, we propose a framework for spectral sensitivity estimation that not only does not require any hardware (including a color target), but also does not require physical access to the camera itself. Similar to other work, we formulate an optimization problem that minimizes a two-term objective function: a camera-specific term from a system of equations, and a universal term that bounds the solution space.
Different than other work, we utilize publicly available high-quality calibration data to construct both terms. We use the colorimetric mapping matrices provided by the Adobe DNG Converter to formulate the camera-specific system of equations, and constrain the solutions using an autoencoder trained on a database of ground-truth curves. On average, we achieve reconstruction errors as low as those that can arise due to manufacturing imperfections between two copies of the same camera. We provide predicted sensitivities for more than 1,000 cameras that the Adobe DNG Converter currently supports, and discuss which tasks can become trivial when camera responses are available.
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Denoisers available in Arnold
https://help.autodesk.com/view/ARNOL/ENU/?guid=arnold_user_guide_ac_denoising_html
AOV denoising: While all denoisers work on arbitrary AOVs, not all denoisers guarantee that the denoised AOVs composite together to match the denoised beauty. The AOV column indicates whether a denoiser has robust AOV denoising and can produce a result where denoised_AOV_1 + denoised_AOV_2 + … + denoised_AOV_N = denoised_Beauty.
OptiX™ Denoiser imager
This imager is available as a post-processing effect. The imager also exposes additional controls for clamping and blending the result. It is based on Nvidia AI technology and is integrated into Arnold for use with IPR and look dev. The OptiX™ denoiser is meant to be used during IPR (so that you get a very quickly denoised image as you’re moving the camera and making other adjustments).
OIDN Denoiser imager
The OIDN denoiser (based on Intel’s Open Image Denoise technology) is available as a post-processing effect. It is integrated into Arnold for use with IPR as an imager (so that you get a very quickly denoised image as you’re moving the camera and making other adjustments).
Arnold Denoiser (Noice)
The Arnold Denoiser (Noice) can be run from a dedicated UI, exposed in the Denoiser, or as an imager, you will need to render images out first via the Arnold EXR driver with variance AOVs enabled. It is also available as a stand-alone program (noice.exe).
This imager is available as a post-processing effect. You can automatically denoise images every time you render a scene, edit the denoising settings and see the resulting image directly in the render view. It favors quality over speed and is, therefore, more suitable for high-quality final frame denoising and animation sequences.
Note:imager_denoiser_noice does not support temporal denoising (required for denoising an animation).
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Virtual Production volumes study
Color Fidelity in LED Volumes
https://theasc.com/articles/color-fidelity-in-led-volumes
Virtual Production Glossary
https://vpglossary.com/What is Virtual Production – In depth analysis
https://www.leadingledtech.com/what-is-a-led-virtual-production-studio-in-depth-technical-analysis/
A comparison of LED panels for use in Virtual Production:
Findings and recommendations
https://eprints.bournemouth.ac.uk/36826/1/LED_Comparison_White_Paper%281%29.pdf -
Stack OverflowAI
https://stackoverflow.blog/2023/07/27/announcing-overflowai/
EDIT
OpenAI and Stack Overflow’s recent partnership announcement has raised concerns for some users that their data is being used without permission. Some users who wanted to delete their answers have had their accounts suspended. The site does not generally allow the deletion of posts if the questions have any answers. Stack Overflow moderators say that it doesn’t allow users to remove posts as they hurt the company as a whole. While the GDPR gives users in the EU the right to be forgotten, websites have a right not to delete data if it doesn’t contain identifiable information to prevent the flow of a topic or question from being disrupted.
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