A robust automatic object segmentation method for 3D printing

Tzu-Kuei Huang, Ying-Hsuang Wang, Ta-Kai Lin, Yung-Yu Chuang

Abstract: 3D printing has become an important and prevalent tool. Image-based modeling is a popular way to acquire 3D models for further editing and printing. However, exiting tools are often not robust enough for users to obtain the 3D models they want. The constructed models are often incomplete, disjoint and noisy. This paper proposes a robust automatic method for segmenting an object out of the background using a set of multi-view images. With the segmentation, 3D reconstruction methods can be applied more robustly. The segmentation is performed by minimizing an energy function which incorporates color statistics, spatial coherency, appearance proximity, epipolar constraints and back projection consistency of 3D feature points. It can be efficiently optimized using the mincut algorithm. Experiments show that the proposed method can generate better models than some popular systems.