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We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net),a learning-based technique for disparity computation in mono-camera structuredlight systems. In our hardware setting, a static pattern is projected onto adynamic scene and captured by a monocular camera. Different from most formerdisparity estimation methods that operate in a frame-wise manner, our networkacquires disparity maps in a temporally incremental way. Specifically, Weexploit the deformation of projected patterns (named pattern flow ) on capturedimage sequences, to model the temporal information. Notably, this newlyproposed pattern flow formulation reflects the disparity changes along theepipolar line, which is a special form of optical flow. Tailored for patternflow, the TIDE-Net, a recurrent architecture, is proposed and implemented. Foreach 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 thefull disparity, as conducted by many previous methods. Interestingly, thisdesign brings clear empirical advantages in terms of efficiency andgeneralization ability. Using only synthetic data for training, our extensitveevaluation results (w.r.t. both accuracy and efficienty metrics) show superiorperformance than several SOTA models on unseen real data. The code is availableon https://github.com/CodePointer/TIDENet.

TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light System