Zero-shot classification of articulating 4D aerial objects through a Markovian synthetic template-matching approach

Computer vision techniques for articulating 3D objects are dependent on learning from large amounts of training data that characterize the full range of object types, appearance, pose relative to the camera, and deformations. To overcome this dependence on exhaustive 2D training sets, we propose a zero-shot 4D template matching approach that learns instantaneous 3D structure from static images and evaluates the likelihood of a temporal deformation model. We fit Hidden Markov Models to a model library of synthetic, 3D animations of aerial objects (bird, drone, parachute, helicopter, flag), and compute the likelihood that a given 4D input fits the process. Using this approach, we demonstrate reliable classification across two domains: synthetic 4D inputs outside the model library and 4D inputs reconstructed from real RGB-D video sequences. Overall, this work establishes an unsupervised, zero-shot approach to non-rigid, aerial object detection that is both effective in practice and inherently extensible, scaling seamlessly to new object types through the simple addition of synthetic animations to the model library.