In this paper, we present an example-driven symbol recognition algorithm based on its key features in CAD engineering drawings. When user provides an example of a specific symbol, the input symbol is analyzed and its features are extracted automatically. Based on the relation representation, the constrained tree with key feature priority can be established for this type of symbol. By this means, the symbol library can be built and expanded automatically in order to handle variety engineering drawings. In the next stage of the recognition processes, we first locate the key feature nodes in drawings, and then find other elements around which satisfy the topology structure of constrained tree. If all the elements and constrains in the tree are found, the symbol object will be recognized. Because of the accurate position, unnecessary matching calculations are greatly reduced. Experimental results validate that our approach is effective.

Sectional views are widely used in engineering practice due to their clear and concise expression. However, it is difficult for computers to understand because of the large numbers of omitted entities and their diversified representations. This paper aims at reconstructing 3D models from 2D sectional views by improving the traditional volume based method. First, we present a two-stage loop searching algorithm to extract desired loops from sectional views. Then, sub-objects are identified by the hint-based feature identification algorithm with an intuitive loop-matching criterion. After that, a model-directed algorithm is proposed to guide the generation of sub-objects which are assembled together to form the final objects. The algorithm can handle full sections, partial sections and offset sections, as well as orthographic views. Multiple sectional views are supported in our algorithm. Moreover, the domain of objects is extended to inclined quadric surfaces and intersecting quadric surfaces with higher order curves. Experiment results show its practicability.

Sectional views are widely used in engineering practice due to their clear and concise expression. However, large numbers of entities are missing in sectional views that makes it hard for computer to understand drawings. Feature identification is the key procedure for solids reconstruction from sectional views. Based on the analysis of the default drawing rules appearing in sectional views, a new algorithm is presented to identify and construct elemental objects in the procedure of volume-based 3D solids reconstruction. In this algorithm, a hint-based search strategy with priority principles is developed for identifying elemental features, even some of them do not have complete expression in 2D views. The algorithm is suitable for full sections, partial sections and offset sections, and there is no restriction on the number of sectional views included in one drawing. Experimental results validate our algorithm.