Non-photorealistic arts have been an invaluable form of media for over tens of thousands of years, and are widely used in animation and games today, motivating research into this field. Recently, the novel 2.5D Model has emerged, targetting the limitations of both 2D and 3D forms of cartoons. The most recent development is the 2.5D Cartoon Model. The manual building process of such models is labour intensive, and no automatic building method for 2.5D models exists currently. This dissertation proposes a novel approach to the problem of automatic creation of 2.5D Cartoon Models, termed Auto-2CM in this thesis, which is the first attempt of a solution to the problem. The proposed approach aims to build 2.5D models from real world objects. Auto-2CM collects 3D information on the candidate object using 3D reconstruction methods from Computer Vision, then partitions it into meaningful parts using segmentation methods from Computer Graphics. A novel 3D-2.5D conversion method is introduced to create the final 2.5D model, which is the first method for 3D-2.5D conversion. The Auto-2CM framework does not mandate specific algorithms of reconstruction or segmentation, therefore different algorithms may be used for different kinds of objects. The effect of different algorithms on the final 2.5D model is currently unknown. A perceptual evaluation of Auto-2CM is performed, which shows that by using different combinations of algorithms within Auto-2CM for specific kinds of objects, the performance of the system maybe increased significantly. The approach can produce acceptable models for both manual sketches and direct use. It is also the first experimental study of the problem.
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