Learning 3D Shape Completion Under Weak Supervision

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Le...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	International journal of computer vision Ročník 128; číslo 5; s. 1162 - 1181
Hlavní autori:	Stutz, David, Geiger, Andreas
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.05.2020 Springer Springer Nature B.V
Predmet:	Artificial Intelligence Artificial neural networks Computer Imaging Computer Science Computer vision Image Processing and Computer Vision Image reconstruction Machine vision Object recognition Pattern Recognition Pattern Recognition and Graphics Robotics Shape optimization Special Issue on Deep Learning for Robotic Vision Supervised learning Supervision Three dimensional models Vision Benchmark 3D reconstruction Weakly-supervised learning 3D shape completion Amortized inference
ISSN:	0920-5691, 1573-1405
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Buďte prvý, kto okomentuje tento záznam!