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...
Những tác giả chính: | Novotny, D, Larlus, D, Vedaldi, A |
---|---|
Định dạng: | Conference item |
Ngôn ngữ: | English |
Được phát hành: |
IEEE
2017
|
Những quyển sách tương tự
-
Capturing the geometry of object categories from video supervision
Bằng: Novotny, D, et al.
Được phát hành: (2018) -
I have seen enough: Transferring parts across categories
Bằng: Novotny, D, et al.
Được phát hành: (2016) -
Learning the semantic structure of objects from Web supervision
Bằng: Novotny, D, et al.
Được phát hành: (2016) -
Unsupervised learning of 3D object categories from videos in the wild
Bằng: Henzler, P, et al.
Được phát hành: (2021) -
NeuralDiff: Segmenting 3D objects that move in egocentric videos
Bằng: Tschernezki, V, et al.
Được phát hành: (2022)