Hierarchical 3D perception from a single image
Inspirited by the human vision mechanism, this paper discusses a hierarchical grammar model for 3D inference of man-made object from a single image. This model decomposes an object with two layers: (i) 3D parts (primitives) with 3D spatial relationship and (ii) 2D aspects with prediction (production) rules. Thus each object is represented by a set of co-related 3D primitives that are generated by a set of 2D aspects. The 3D relationships can be learned for each object category specifically by a discriminative boosting method, and the 2D production rules are defined according to the human visual experience. With this representation, the inference follows a data-driven Markov Chain Monte Carlo computing method in the Bayesian framework. In the experiments, we demonstrate the 3D inference results on 8 object categories and also propose a psychology analysis to evaluate our work.