Perceptual deep depth super-resolution

Voynov, Oleg; Alexey, Artemov; Egiazarian, Vage; Notchenko, Alexandr; Bobrovskikh, Gleb; Evgeny, Burnaev; Denis, Zorin

doi:10.48550/arxiv.1812.09874

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

Publication Types

Select...

Book1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the guidance of an appropriately designed directional field, both topological (e.g., placement of singularity points) and geometric (e.g., smoothness) properties of the underlying geometric structure may be efficiently derived. Other applications which could benefit from learnable directional fields include remote sensing [11,3,14,9], RGBD data processing [19] and related applications [1,5], shape retrieval [13]. However, obtaining a robust approximation of a directional field from raw input data is a challenging problem in many instances.…”

Section: Introductionmentioning

confidence: 99%

Learning to Approximate Directional Fields Defined Over 2D Planes

Taktasheva

Matveev

Artemov

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Reconstruction of directional fields is a need in many geometry processing tasks, such as image tracing, extraction of 3D geometric features, and finding principal surface directions. A common approach to the construction of directional fields from data relies on complex optimization procedures, which are usually poorly formalizable, require a considerable computational effort, and do not transfer across applications. In this work, we propose a deep learning-based approach and study the expressive power and generalization ability.

show abstract