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This paper presents a novel modeling framework to build 3D models of Chinese architectures from elevation drawing. Our algorithm integrates the capability of automatic drawing recognition with powerful procedural modeling to extract production rules from elevation drawing. First, different from the previous symbol-based floor plan recognition, based on the novel concept of repetitive pattern trees, small horizontal repetitive regions of the elevation drawing are clustered in a bottom-up manner to form architectural components with maximum repetition, which collectively serve as building blocks for 3D model generation. Second, to discover the global architectural structure and its components' interdependencies, the components are structured into a shape tree in a top-down subdivision manner and recognized hierarchically at each level of the shape tree based on Markov Random Fields (MRFs). Third, shape grammar rules can be derived to construct 3D semantic model and its possible variations with the help of a 3D component repository. The salient contribution lies in the novel integration of procedural modeling with elevation drawing, with a unique application to Chinese architectures.