Human Correspondence Consensus for 3D Object Semantic Understanding

Published in ECCV, 2020

We observe that people have a consensus on semantic correspondences between two areas from different objects, but are less certain about the exact semantic meaning of each area. Therefore, we argue that by providing human labeled correspondences between different objects from the same category instead of explicit semantic labels, one can recover rich semantic information of an object. In this paper, we introduce a new dataset named CorresPondenceNet. Based on this dataset, we are able to learn dense semantic embeddings with a novel geodesic consistency loss.

Recommended citation: Lou, Y., You, Y., Li, C., Cheng, Z., Li, L., Ma, L., ... & Lu, C. (2020, August). Human Correspondence Consensus for 3D Object Semantic Understanding. In European Conference on Computer Vision (pp. 496-512). Springer, Cham.