TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in
  Structured Light System
Rukun Qiao Hiroshi Kawasaki Hongbin Zha
We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net),
a learning-based technique for disparity computation in mono-camera structured
light systems. In our hardware setting, a static pattern is projected onto a
dynamic scene and captured by a monocular camera. Different from most former
disparity estimation methods that operate in a frame-wise manner, our network
acquires disparity maps in a temporally incremental way. Specifically, We
exploit the deformation of projected patterns (named pattern flow ) on captured
image sequences, to model the temporal information. Notably, this newly
proposed pattern flow formulation reflects the disparity changes along the
epipolar line, which is a special form of optical flow. Tailored for pattern
flow, the TIDE-Net, a recurrent architecture, is proposed and implemented. For
each incoming frame, our model fuses correlation volumes (from current frame)
and disparity (from former frame) warped by pattern flow. From fused features,
the final stage of TIDE-Net estimates the residual disparity rather than the
full disparity, as conducted by many previous methods. Interestingly, this
design brings clear empirical advantages in terms of efficiency and
generalization ability. Using only synthetic data for training, our extensitve
evaluation results (w.r.t. both accuracy and efficienty metrics) show superior
performance than several SOTA models on unseen real data. The code is available
on https://github.com/CodePointer/TIDENet.