Andrea Vedaldi: Learning 3D objects in the real world
Автор: SIYUAN HUANG
Загружено: 2021-06-25
Просмотров: 1394
Reconstructing and interpreting real world scenes in 3D is a major challenge, especially when this needs to be done form scarce data, such as a single or few input images or scans. For this to be possible at all, we require rich 3D priors that can constrain reconstruction. In this talk, I will discuss progress in Facebook AI Research in developing such priors. I will introduce Common Objects in 3D, a new dataset of videos of real objects, from which 3D priors can be acquired. I will then discuss algorithms to reconstruct new objects from a small number of images, including warp-conditioned ray embedding and NeRFormer. Finally, I will discuss the general problem of establishing 3D correspondences in complex object categories, which is a key step towards building better 3D priors, and introduce NeuroMorph, a method to establish correspondences in an unsupervised manner.
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