RGB-D Video Mirror Detection

Mingchen Xu, Peter Herbert, Yu-Kun Lai, Ze Ji, Jing Wu; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 9622-9631

Mirror detection aims to identify mirror areas in a scene with recent methods either integrating depth information (RGB-D) or making use of temporal information (video). However utilizing both data is still under-explored due to the lack of a high-quality dataset and an effective method for the RGB-D Video Mirror Detection (DVMD) problem. To the best of our knowledge this is the first work to address the DVMD problem. To exploit depth and temporal information in mirror segmentation we first construct a large-scale RGB-D Video Mirror Detection Dataset (DVMD-D) which contains 17977 RGB-D images from 273 diverse videos. We further develop a novel model named DVMDNet which can first locate the mirrors based on triple consistencies: local consistency cross-modality consistency and global consistency and then refine the mirror boundaries through content discontinuity taking the temporal information within videos into account. We conduct a comparative study on the DVMD dataset evaluating 12 state-of-the art models (including single-image mirror detection single-image glass detection RGB-D mirror detection video shadow detection video glass detection and video mirror detection methods). Code is available from https://github.com/UpChen/2025_DVMDNet.