  Transparent objects such as windows and bottles made by glass widely exist in
the real world. Segmenting transparent objects is challenging because these
objects have diverse appearance inherited from the image background, making
them had similar appearance with their surroundings. Besides the technical
difficulty of this task, only a few previous datasets were specially designed
and collected to explore this task and most of the existing datasets have major
drawbacks. They either possess limited sample size such as merely a thousand of
images without manual annotations, or they generate all images by using
computer graphics method (i.e. not real image). To address this important
problem, this work proposes a large-scale dataset for transparent object
segmentation, named Trans10K, consisting of 10,428 images of real scenarios
with carefully manual annotations, which are 10 times larger than the existing
datasets. The transparent objects in Trans10K are extremely challenging due to
high diversity in scale, viewpoint and occlusion as shown in Fig. 1. To
evaluate the effectiveness of Trans10K, we propose a novel boundary-aware
segmentation method, termed TransLab, which exploits boundary as the clue to
improve segmentation of transparent objects. Extensive experiments and ablation
studies demonstrate the effectiveness of Trans10K and validate the practicality
of learning object boundary in TransLab. For example, TransLab significantly
outperforms 20 recent object segmentation methods based on deep learning,
showing that this task is largely unsolved. We believe that both Trans10K and
TransLab have important contributions to both the academia and industry,
facilitating future researches and applications.
