Learning Universal Vector Representation for Objects of Different 3D Euclidean formats

We present a method for learning universal vector representations out of 3D objects represented in different data formats. A newly proposed switching mechanism is used in the design of neural network architecture. During the learning process, the encoder for one specific format also learns to perceive the object from the perspective of other formats, hence the learned universal representation contains richer information. With the learned universal representation, it would be possible to "translate" between different 3D shape formats of the input object since they share similar embedding of 3D information. Higher performance can also be achieved for the 3D data synthetic tasks with this method.