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Capturing the geometry of object categories from video supervision
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| David Novotny, Diane Larlus, Andrea Vedaldi |
| Pattern Analysis and Machine Intelligence, September 2018 |
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@article{novotny18pami,
author = {Novotny, David and Larlus, Diane and Vedaldi, Andrea},
year = {2018},
month = {09},
pages = {1-1},
title = {Capturing the Geometry of Object Categories from Video Supervision},
volume = {PP},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/TPAMI.2018.2871117}
}
In this article, we are interested in capturing the 3D geometry of object categories simply by looking around them. Our unsupervised method fundamentally departs from traditional approaches that require either CAD models or manual supervision. It only uses video sequences capturing a handful of instances of an object category to train a deep architecture tailored for extracting 3D geometry predictions. Our deep architecture has three components. First, a Siamese viewpoint factorization network robustly aligns the input videos and, as a consequence, learns to predict the absolute category-specific viewpoint from a single image depicting any previously unseen instance of that category. Second, a depth estimation network performs monocular depth prediction. Finally, a 3D shape completion network predicts the full shape of the depicted object instance by re-using the output of the monocular depth prediction module. We also propose a way to configure networks so they can perform probabilistic predictions. We demonstrate that, properly used in our framework, this self-assessment mechanism is crucial for obtaining high quality predictions. Our network achieves state-of-the-art results on viewpoint prediction, depth estimation, and 3D point cloud estimation on public benchmarks.
This is an extended version of the following ICCV17 conference paper. More information on the VGG webpage.
BibTex reference:
@article{novotny18pami,
author = {Novotny, David and Larlus, Diane and Vedaldi, Andrea},
year = {2018},
month = {09},
pages = {1-1},
title = {Capturing the Geometry of Object Categories from Video Supervision},
volume = {PP},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/TPAMI.2018.2871117}
}
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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