3 ms·
> "RAW inputs improve prior methods, but our system outperforms them." I understand why RAW is useful in general and why all methods would benefit (i.e. higher
by CharlesW 3y ago
> "RAW inputs improve prior methods, but our system outperforms them."
I understand why RAW is useful in general and why all methods would benefit (i.e. higher dynamic range, >8bpc color depth), but I don't understand how this system disproportionately benefits from that.
Is it because the models used in this system are trained from RAW, where they're not in other systems?
- GrantMoyer 3y agoMy guess: raw inputs preserve the linearity of radiance at each pixel. In other words, for a linear function f, f(Total Radiance) = f(Base Radiance + Reflected Radiance) → f(Total Radiance) = f(Base Radiance) + f(Reflected Radiance). Conversion from raw to another format may introduce a non-linear map on total radiance to compress the range to 8 bits while preserving contrast in most of the image (particularly for parts of the image washed out by a bright reflection). So with raw images, the value you need to find is f(Reflected Radiance), which is probably why having a reference photo in the reflected direction helps. On the other hand, for other formats the reflection component of the image isn't a simple linear transform of whats being reflected, so even with a reference image, the reflection component would be hard to determine.
- Derbasti 3y agoMaybe in this case, because these are phone pictures, which are quite heavily processed (sharpening, denoising, tone mapping, local white balance, local contrast). The raw image may contain a bit less of that stuff.