  One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. In this paper, we impose multiple projection patterns into each single
captured image to realize temporal super resolution of the depth image
sequences. With our method, multiple patterns are projected onto the object
with higher fps than possible with a camera. In this case, the observed pattern
varies depending on the depth and motion of the object, so we can extract
temporal information of the scene from each single image. The decoding process
is realized using a learning-based approach where no geometric calibration is
needed. Experiments confirm the effectiveness of our method where sequential
shapes are reconstructed from a single image. Both quantitative evaluations and
comparisons with recent techniques were also conducted.
