A review of computer graphics approaches to urban modeling from a machine learning perspective

2021 ◽  
Author(s):  
Tian Feng ◽  
Feiyi Fan ◽  
Tomasz Bednarz
Keyword(s):  
Machine Learning ◽  
Computer Graphics ◽  
Urban Modeling

Virtual Reality and Point-Based Rendering in Architecture and Heritage

2018 ◽  
pp. 549-565
Author(s):  
Omar A. Mures ◽  
Alberto Jaspe ◽  
Emilio J. Padrón ◽  
Juan R. Rabuñal
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Computer Graphics ◽  
Civil Engineering ◽  
Point Clouds ◽  
Recent Advances ◽  
Ancient Monuments

Recent advances in acquisition technologies such as LiDAR, range cameras and photogrammetry have put point clouds once again in the forefront of several fields with applications in Computer Graphics, Vision and Machine Learning, such as civil engineering, architecture, heritage and archaeology. Taking also into account new progressions in Virtual Reality that are also making VR relevant again, the possibilities when using these two technologies together are endless. From the improvement of architectural workflows, to the conservation of important ancient monuments, these two technologies can improve current efforts substantially. This chapter focuses on how these two fields can be combined in new and innovative ways, so that professionals can optimally exploit the advantages that these improved technologies can offer.

scholarly journals Computing the Maximum Bichromatic Discrepancy, with Applications to Computer Graphics and Machine Learning

1996 ◽  
Vol 52 (3) ◽  
pp. 453-470 ◽  
Author(s):  
David P. Dobkin ◽  
Dimitrios Gunopulos ◽  
Wolfgang Maass
Keyword(s):  
Machine Learning ◽  
Computer Graphics

Virtual Reality and Point-Based Rendering in Architecture and Heritage

2016 ◽  
pp. 78-95 ◽  
Author(s):  
Omar A. Mures ◽  
Alberto Jaspe ◽  
Emilio J. Padrón ◽  
Juan R. Rabuñal
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Computer Graphics ◽  
Civil Engineering ◽  
Point Clouds ◽  
Recent Advances ◽  
Ancient Monuments

Recent advances in acquisition technologies such as LiDAR, range cameras and photogrammetry have put point clouds once again in the forefront of several fields with applications in Computer Graphics, Vision and Machine Learning, such as civil engineering, architecture, heritage and archaeology. Taking also into account new progressions in Virtual Reality that are also making VR relevant again, the possibilities when using these two technologies together are endless. From the improvement of architectural workflows, to the conservation of important ancient monuments, these two technologies can improve current efforts substantially. This chapter focuses on how these two fields can be combined in new and innovative ways, so that professionals can optimally exploit the advantages that these improved technologies can offer.

Enhancing computer graphics through machine learning: a survey

2006 ◽  
Vol 23 (1) ◽  
pp. 25-43 ◽  
Author(s):  
Jonathan Dinerstein ◽  
Parris K. Egbert ◽  
David Cline
Keyword(s):  
Machine Learning ◽  
Computer Graphics

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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