Image-driven unsupervised 3D model co-segmentation

Segmentation of 3D models is a fundamental task in computer graphics and vision. Geometric methods usually lead to non-semantic and fragmentary segmentations. Learning techniques require a large amount of labeled training data. In this paper, we explore the feasibility of 3D model segmentation by taking advantage of the huge number of easy-to-obtain 2D realistic images available on the Internet. The regional color exhibited in images provides information that is valuable for segmentation. To transfer the segmentations, we first filter out inappropriate images with several criteria. The views of these images are estimated by our proposed texture-invariant view estimation Siamese network. The training samples are generated by rendering-based synthesis without laborious labeling. Subsequently, we transfer and merge the segmentations produced by each individual image by applying registration and a graph-based aggregation strategy. The final result is obtained by combining all segmentations within the 3D model set. Our qualitative and quantitative experimental results on several model categories validate effectiveness of our proposed method.