Specular Surface Detection with Deep Static Specular Flow and Highlight

Hirotaka Hachiya, Yuto Yoshimura

Abstract: To apply robot teaching to a factory with many mirror-polished parts, it is necessary to detect the specular surface accurately. Deep models for mirror detection have been studied by designing mirror-specific features, e.g., contextual contrast and similarity. However, mirror-polished parts such as plastic molds, tend to have complex shapes and ambiguous boundaries, and thus, existing mirror-specific deep features could not work well. To overcome the problem, we propose introducing attention maps based on the concept of static specular flow (SSF), condensed reflections of the surrounding scene, and specular highlight (SH), bright light spots, frequently appearing even in complex-shaped specular surfaces and applying them to deep model-based multi-level features. Then, we adaptively integrate approximated mirror maps generated by multi-level SSF, SH, and existing mirror detectors to detect complex specular surfaces. Through experiments with our original data sets with spherical mirrors and real-world plastic molds, we show the effectiveness of the proposed method.