  Semi-supervised learning via teacher-student network can train a model
effectively on a few labeled samples. It enables a student model to distill
knowledge from the teacher's predictions of extra unlabeled data. However, such
knowledge flow is typically unidirectional, having the performance vulnerable
to the quality of teacher model. In this paper, we seek to robust 3D
reconstruction of stereo endoscopic images by proposing a novel fashion of
bidirectional learning between two learners, each of which can play both roles
of teacher and student concurrently. Specifically, we introduce two
self-supervisions, i.e., Adaptive Cross Supervision (ACS) and Adaptive Parallel
Supervision (APS), to learn a dual-branch convolutional neural network. The two
branches predict two different disparity probability distributions for the same
position, and output their expectations as disparity values. The learned
knowledge flows across branches along two directions: a cross direction
(disparity guides distribution in ACS) and a parallel direction (disparity
guides disparity in APS). Moreover, each branch also learns confidences to
dynamically refine its provided supervisions. In ACS, the predicted disparity
is softened into a unimodal distribution, and the lower the confidence, the
smoother the distribution. In APS, the incorrect predictions are suppressed by
lowering the weights of those with low confidence. With the adaptive
bidirectional learning, the two branches enjoy well-tuned supervisions, and
eventually converge on a consistent and more accurate disparity estimation. The
extensive and comprehensive experimental results on four public datasets
demonstrate our superior performance over other state-of-the-arts with a
relative decrease of averaged disparity error by at least 9.76%.
