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Visual semantic correspondence is an important topic in computer vision andcould help machine understand objects in our daily life. However, most previousmethods directly train on correspondences in 2D images, which is end-to-end butloses plenty of information in 3D spaces. In this paper, we propose a newmethod on predicting semantic correspondences by leveraging it to 3D domain andthen project corresponding 3D models back to 2D domain, with their semanticlabels. Our method leverages the advantages in 3D vision and can explicitlyreason about objects self-occlusion and visibility. We show that our methodgives comparative and even superior results on standard semantic benchmarks. Wealso conduct thorough and detailed experiments to analyze our networkcomponents. The code and experiments are publicly available athttps://github.com/qq456cvb/SemanticTransfer.

Semantic Correspondence via 2D-3D-2D Cycle