  Transparent objects are widely used in industrial automation and daily life.
However, robust visual recognition and perception of transparent objects have
always been a major challenge. Currently, most commercial-grade depth cameras
are still not good at sensing the surfaces of transparent objects due to the
refraction and reflection of light. In this work, we present a
transformer-based transparent object depth estimation approach from a single
RGB-D input. We observe that the global characteristics of the transformer make
it easier to extract contextual information to perform depth estimation of
transparent areas. In addition, to better enhance the fine-grained features, a
feature fusion module (FFM) is designed to assist coherent prediction. Our
empirical evidence demonstrates that our model delivers significant
improvements in recent popular datasets, e.g., 25% gain on RMSE and 21% gain on
REL compared to previous state-of-the-art convolutional-based counterparts in
ClearGrasp dataset. Extensive results show that our transformer-based model
enables better aggregation of the object's RGB and inaccurate depth information
to obtain a better depth representation. Our code and the pre-trained model
will be available at https://github.com/yuchendoudou/TODE.
