Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning

Shadows encode rich information about scene geometry andillumination, yet existing methods either predict a unified shadow maskor overlook attached shadows entirely. We address this gap by propos-ing a framework for jointly detecting cast and attached shadows throughexplicit physical modeling of light direction and surface geometry un-der a dominant directional-light setting. Our approach is grounded in asimple observation: surfaces facing away from the light source tend tofall into shadow. We exploit the reciprocal relationship between shadowformation and light estimation to construct a closed feedback loop, adual-module architecture in which a shadow detection module and alight estimation module iteratively refine each other. At each pass, up-dated light estimates, together with surface normals, produce partial at-tached shadow maps that guide detection, while improved shadow pre-dictions sharpen light estimation. To support training and evaluation,we introduce a dataset of 1,458 images with manually annotated castand attached shadow masks sourced from three existing benchmarks.Experiments demonstrate that our proposed method outperforms priormethods, with at least a 33% reduction in attached-shadow BER, whilemaintaining strong full-shadow and cast-shadow performance. Projectpage: https://shilin21.github.io/attached_detection/