Cast and Attached Shadow Detection via Iterative Light and Geometry Reasoning

TL;DR

This paper introduces an iterative, physically grounded framework for jointly detecting cast and attached shadows by leveraging light and geometry reasoning, significantly improving shadow detection accuracy.

Contribution

It proposes a novel dual-module architecture that iteratively refines shadow detection and light estimation, explicitly modeling physical light and surface geometry.

Findings

Achieves at least 33% reduction in attached shadow error rate

Outperforms prior methods in shadow detection accuracy

Introduces a new dataset with annotated shadows

Abstract

Shadows encode rich information about scene geometry and illumination, yet existing methods either predict a unified shadow mask or overlook attached shadows entirely. We address this gap by proposing a framework for jointly detecting cast and attached shadows through explicit physical modeling of light direction and surface geometry. Our approach is grounded in a simple observation: surfaces facing away from the light source tend to fall into shadow. We exploit the reciprocal relationship between shadow formation and light estimation to construct a closed feedback loop, a dual-module architecture in which a shadow detection module and a light estimation module iteratively refine each other. At each pass, updated light estimates with surface normals produce partial attached shadow maps that guide detection, while improved shadow predictions sharpen light estimation. To support training and evaluation, we introduce a dataset of 1,458 images with manually annotated cast and attached shadow masks sourced from three existing benchmarks. Experiments demonstrate that our physically grounded, iterative formulation outperforms prior methods, with at least a 33% reduction in attached BER, while maintaining strong full and cast performance.