Výsledky vyhledávání

Depth Prediction without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

Autor: Soeren Pirk, Anelia Angelova, Vincent Casser, Reza Mahjourian

Publikováno v: AAAI

Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and robot ego-motion where supervision is provided by monocular videos, a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb9ee8fd9f2f0bc180d6387c7fcabfa9
https://doi.org/10.1609/aaai.v33i01.33018001

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Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics

Autor: Soeren Pirk, Vincent Casser, Reza Mahjourian, Anelia Angelova

Publikováno v: CVPR Workshops

We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of individual objects and learn their 3D motion vector jointly with depth

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af95b729647cb53f8f75331e36f03f44
http://arxiv.org/abs/1906.05717

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Parsing Geometry Using Structure-Aware Shape Templates

Autor: Olga Diamanti, Matthias Niessner, Soeren Pirk, Leonidas J. Guibas, Vignesh Ganapathi-Subramanian, Chengcheng Tang

Publikováno v: 3DV

Real-life man-made objects often exhibit strong and easily-identifiable structure, as a direct result of their design or their intended functionality. Structure typically appears in the form of individual parts and their arrangement. Knowing about ob

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e93dabfba2d98eb17ec8ec4eebfce7a8

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3D convolutional neural networks by modal fusion

Autor: Leonidas J. Guibas, Soeren Pirk, Eiichi Yoshida, Yusuke Yoshiyasu

Publikováno v: ICIP

We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing betwe

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e1eab04f1791b66f3584fd53955e6de6
https://doi.org/10.1109/icip.2017.8296587

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Texture-lobes for tree modeling

Autor: Daniel Cohen-Or, Baoquan Chen, Zhanglin Cheng, Yotam Livny, Feilong Yan, Oliver Deussen, Soeren Pirk

We present a lobe-based tree representation for modeling trees. The new representation is based on the observation that the tree’s foliage details can be abstracted into canonical geometry structures, termed lobe-textures. We introduce techniques t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cea90d9eeedc3a6c27cff227b18c3ee5

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