跨传感器RAW目标检测新框架，提升复杂光照下的识别精度。
RAWild: Sensor-Agnostic RAW Object Detection via Physics-Guided Curve and Grid Modeling
- 用物理引导的全局-局部色调映射，统一不同传感器数据差异
- 在10-24位RAW数据上实现超越现有方法的检测性能
- 适合需要跨设备通用化的目标检测场景
相机传感器原始数据（RAW）具备更深位深、保留物理信息及免于图像信号处理器（ISP）失真的优势。但设备间曝光条件、光谱响应和位深差异导致的领域差距远大于sRGB，使跨传感器泛化成为核心挑战。本文提出 extbf{RAWild}，一种基于物理引导的全局-局部色调映射框架，通过将传感器引起的差异分解为全局亮度校正与空间自适应局部色彩调整，均基于RAW分布先验，使单一网络可联合训练于异构传感器数据。为进一步支持跨传感器泛化，构建了基于物理的RAW仿真流水线，合成涵盖多种光谱响应、光照条件及传感器非理想特性的真实感输出。在多个覆盖10-24位深度的RAW基准测试中，无论单数据集、混合数据集或鲁棒性挑战场景，均达到当前最优（SOTA）性能。
原文摘要 · Abstract (English)
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.
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