Abstract: Semantic mesh segmentation and labeling is a fundamental problem in graphics. Conventional data-driven approaches usually employ a tedious offline pre-training process. Moreover, the number and especially the quality of the manually labeled examples challenge such strategies. In this paper, we develop a low-rank representation model with structure guiding to address these problems. The pre-training step is successfully eliminated and a test mesh can be labeled just using a few examples. As consistently labeling a large amount of meshes manually is a tedious procedure accompanied by inevitable mislabelings, our method is indeed more suitable for semantic mesh segmentation and labeling in real situations. In additional, by introducing the guiding from geometric similarity and labeling structure, and the robust ℓ 2 , 1 norm, our method generates correct labeling, even when the set of given examples contains multiple object categories or mislabeled meshes. Experimental results on the Princeton Segmentation Benchmark show that our approach outperforms the existing learning based methods.