From volumes to views: an approach to 3-D object recognition
CVGIP: Image Understanding - Special issue on directions in CAD-based vision
Part-Based 3D Descriptions of Complex Objects from a Single Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Reconstruction of 3D Shapes and Scenes From A Single Image
HLK '03 Proceedings of the First IEEE International Workshop on Higher-Level Knowledge in 3D Modeling and Motion Analysis
Primal sketch: Integrating structure and texture
Computer Vision and Image Understanding
What the Back of the Object Looks Like: 3D Reconstruction from Line Drawings without Hidden Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bottom-Up/Top-Down Image Parsing with Attribute Grammar
IEEE Transactions on Pattern Analysis and Machine Intelligence
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Using grammars for pattern recognition in images: A systematic review
ACM Computing Surveys (CSUR)
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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.