  Transparent objects are common in daily life. However, depth sensing for
transparent objects remains a challenging problem. While learning-based methods
can leverage shape priors to improve the sensing quality, the labor-intensive
data collection in the real world and the sim-to-real domain gap restrict these
methods' scalability. In this paper, we propose a method to finetune a stereo
network with sparse depth labels automatically collected using a probing system
with tactile feedback. We present a novel utility function to evaluate the
benefit of touches. By approximating and optimizing the utility function, we
can optimize the probing locations given a fixed touching budget to better
improve the network's performance on real objects. We further combine tactile
depth supervision with a confidence-based regularization to prevent
over-fitting during finetuning. To evaluate the effectiveness of our method, we
construct a real-world dataset including both diffuse and transparent objects.
Experimental results on this dataset show that our method can significantly
improve real-world depth sensing accuracy, especially for transparent objects.
