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Learning 3D Object Categories by Looking Around Them
lib:48b6b973f48825e6 (v1.0.0)
Authors: David Novotny,Diane Larlus,Andrea Vedaldi
Where published: ICCV 2017 10
ArXiv: 1705.03951
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1705.03951v2
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 viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.
Where published: ICCV 2017 10
ArXiv: 1705.03951
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1705.03951v2
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 viewpoint factorization network that robustly aligns different videos together without explicitly comparing 3D shapes; and a 3D shape completion network that can extract the full shape of an object from partial observations. We also demonstrate the benefits of configuring networks to perform probabilistic predictions as well as of geometry-aware data augmentation schemes. We obtain state-of-the-art results on publicly-available benchmarks.
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