TROSD: A New RGB-D Dataset for Transparent and Reflective Object Segmentation in Practice
Tianyu Sun, Guodong Zhang, Wenming Yang, Jing‐Hao Xue, Guijin Wang
Abstract
Transparent and reflective objects are omnipresent in our daily life, but their unique visual and optical characteristics are notoriously challenging even for state-of-the-art deep networks of semantic segmentation. To alleviate this challenge, we construct a new large-scale real-world RGB-D dataset called TROSD, which is more comprehensive than existing datasets for transparent and reflective object segmentation. Our TROSD dataset contains 11,060 RGB-D images with three semantic classes in terms of transparent objects, reflective objects, and others, covering a variety of daily scenes. Together with the dataset, we also introduce a novel network (TROSNet) as a high-standard baseline to assist other researchers to develop and benchmark their algorithms of transparent and reflective object segmentation. Moreover, extensive experiments also clearly show that the proposed TROSD dataset has an excellent capacity to facilitate the development of semantic segmentation algorithms with strong generalizability.