Learning 3D object categories by looking around them
Traditional approaches for learning 3D object categories use either synthetic data or manual supervision. In this paper, we propose a method which does not require manual annotations and is instead cued by observing objects from a moving vantage point. Our system builds on two innovations: a Siamese...
Hoofdauteurs: | Novotny, D, Larlus, D, Vedaldi, A |
---|---|
Formaat: | Conference item |
Taal: | English |
Gepubliceerd in: |
IEEE
2017
|
Gelijkaardige items
-
Capturing the geometry of object categories from video supervision
door: Novotny, D, et al.
Gepubliceerd in: (2018) -
I have seen enough: Transferring parts across categories
door: Novotny, D, et al.
Gepubliceerd in: (2016) -
Learning the semantic structure of objects from Web supervision
door: Novotny, D, et al.
Gepubliceerd in: (2016) -
Unsupervised learning of 3D object categories from videos in the wild
door: Henzler, P, et al.
Gepubliceerd in: (2021) -
NeuralDiff: Segmenting 3D objects that move in egocentric videos
door: Tschernezki, V, et al.
Gepubliceerd in: (2022)