www.xuanprada.com/blog/2014/11/3/hdri-shooting

 

http://blog.gregzaal.com/2016/03/16/make-your-own-hdri/

 

http://blog.hdrihaven.com/how-to-create-high-quality-hdri/

 

Shooting checklist

• Full coverage of the scene (fish-eye shots)
• Backplates for look-development (including ground or floor)
• Macbeth chart for white balance
• Grey ball for lighting calibration
• Chrome ball for lighting orientation
• Basic scene measurements
• Material samples
• Individual HDR artificial lighting sources if required

Methodology

• Plant the tripod where the action happens, stabilise it and level it
• Set manual focus
• Set white balance
• Set ISO
• Set raw+jpg
• Set apperture
• Metering exposure
• Set neutral exposure
• Read histogram and adjust neutral exposure if necessary
• Shot slate (operator name, location, date, time, project code name, etc)
• Set auto bracketing
• Shot 5 to 7 exposures with 3 stops difference covering the whole environment
• Place the aromatic kit where the tripod was placed, and take 3 exposures. Keep half of the grey sphere hit by the sun and half in shade.
• Place the Macbeth chart 1m away from tripod on the floor and take 3 exposures
• Take backplates and ground/floor texture references
• Shoot reference materials
• Write down measurements of the scene, specially if you are shooting interiors.
• If shooting artificial lights take HDR samples of each individual lighting source.

Exposures starting point

• Day light sun visible ISO 100 F22
• Day light sun hidden ISO 100 F16
• Cloudy ISO 320 F16
• Sunrise/Sunset ISO 100 F11
• Interior well lit ISO 320 F16
• Interior ambient bright ISO 320 F10
• Interior bad light ISO 640 F10
• Interior ambient dark ISO 640 F8
• Low light situation ISO 640 F5

 

NOTE: The goal is to clean the initial individual brackets before or at merging time as much as possible.
This means:

• keeping original shooting metadata
• de-fringing
• removing aberration (through camera lens data or automatically)
• at 32 bit
• in ACEScg (or ACES) wherever possible

 

https://www.studiobinder.com/blog/what-is-dynamic-range-photography/

 

https://www.hdrsoft.com/resources/dri.html#bit-depth

 

 

 

The dynamic range is a ratio between the maximum and minimum values of a physical measurement. Its definition depends on what the dynamic range refers to.

For a scene: Dynamic range is the ratio between the brightest and darkest parts of the scene.

 

For a camera: Dynamic range is the ratio of saturation to noise. More specifically, the ratio of the intensity that just saturates the camera to the intensity that just lifts the camera response one standard deviation above camera noise.

 

For a display: Dynamic range is the ratio between the maximum and minimum intensities emitted from the screen.

 

 

 

 

 

https://diffusionlight.github.io/

 

 

https://github.com/DiffusionLight/DiffusionLight

 

https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme

 

https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S

 

“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”