RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling

TL;DR

RAWild introduces a physics-guided tone mapping framework that enables sensor-agnostic RAW object detection, effectively handling variations across diverse camera sensors and improving robustness and generalization.

Contribution

The paper proposes a novel physics-guided RAW processing framework and a simulation pipeline to enhance sensor-agnostic object detection in RAW images.

Findings

Achieves state-of-the-art performance on multiple RAW benchmarks.

Effectively generalizes across heterogeneous sensors and conditions.

Demonstrates robustness in challenging detection scenarios.

Abstract

Camera sensor RAW data offers intrinsic advantages for object detection, including deeper bit depth, preserved physical information, and freedom from image signal processor (ISP) distortions. However, varying exposure conditions, spectral sensitivities, and bit depths across devices introduce substantially larger domain gaps than sRGB, making sensor-agnostic generalization a fundamental challenge. In this study, we present \textbf{RAWild}, a physics-guided global-local tone mapping framework for sensor-agnostic RAW object detection. By factoring sensor-induced variations into a global tonal correction and a spatially adaptive local color adjustment, both driven by RAW distribution priors, our framework enables a single network to train jointly across heterogeneous sensors. To further support cross-sensor generalization, we construct a physics-based RAW simulation pipeline that synthesizes realistic sensor outputs spanning diverse spectral sensitivities, illuminants, and sensor non-idealities. Extensive experiments across multiple RAW benchmarks covering bit depths from 10 to 24 demonstrate state-of-the-art (SOTA) performance under single-dataset, mixed-dataset, and challenging robustness settings.