# Beyond Calibration: Physically Informed Learning for Raw-to-Raw Mapping

arXiv: https://arxiv.org/abs/2506.08650 · HTML: https://mlsanity.com/paper/2506.08650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Unpaired Image-to-Image Translation using Adversarial Consistency Loss](https://mlsanity.com/paper/2003.04858/) (2020)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [Cascading Convolutional Color Constancy](https://mlsanity.com/paper/1912.11180/) (2019)
- [Semi-Supervised Raw-to-Raw Mapping](https://mlsanity.com/paper/2106.13883/) (2021)
- [Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation](https://mlsanity.com/paper/2209.05968/) (2022)
- [Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs](https://mlsanity.com/paper/2404.10700/) (2024)
- [Dual-Camera Smooth Zoom on Mobile Phones](https://mlsanity.com/paper/2404.04908/) (2024)
- [Spectral Sensitivity Estimation Without a Camera](https://mlsanity.com/paper/2304.11549/) (2023)
- [Color Homography](https://mlsanity.com/paper/1605.04250/) (2016)

## Built on by (most cited first, 1 of 1)

- [Color Pass-Through via Camera-Display Coupling](https://mlsanity.com/paper/2607.12746/) (2026)

## Abstract (arXiv)

Achieving consistent color reproduction across multiple cameras is essential for seamless image fusion and Image Processing Pipeline (ISP) compatibility in modern devices, but it is a challenging task due to variations in sensors and optics. Existing raw-to-raw conversion methods face limitations such as poor adaptability to changing illumination, high computational costs, or impractical requirements such as simultaneous camera operation and overlapping fields-of-view. We introduce the Neural Physical Model (NPM), a lightweight, physically-informed approach that simulates raw images under specified illumination to estimate transformations between devices. The NPM effectively adapts to varying illumination conditions, can be initialized with physical measurements, and supports training with or without paired data. Experiments on public datasets like NUS and BeyondRGB demonstrate that NPM outperforms recent state-of-the-art methods, providing robust chromatic consistency across different sensors and optical systems.