Motion estimation of partially viewed 3-D objects based on a continuous distance transform neural network

Proposes a novel continuous distance transform neural network (CDTNN) to implement a continuous parametric 3D distance transform representation, which can be efficiently used in estimating the motion of 3D objects from a single view perspective. Our proposed CDTNN motion estimation approach consists of two stages of efforts. The 3D object is first converted by a CDTNN to a continuous 3D distance transform representation, i.e. the magnitudes of the responding neural network output values are linearly proportional to the distance of the points to the nearest surface of the object. When later presented with surface points of the oriented snapshots of a moving 3D object, this parametric CDTNN representation allows easy accumulation of the orientation mismatch information. More specifically, the mismatch information can be back-propagated through the CDTNN to iteratively determine the best similarity transform (orientation) required to align the oriented object with the represented exemplar object at each time instance. This orientation provide enough information for the motion of the estimated 3D object