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Architectural Rendering and the Slippery Slope of the Uncanny Valley


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Fifty-one years ago, in 1970, a Japanese roboticist named Masahiro Mori came up with the concept of the “Uncanny Valley”. Around the same time, architectural renderings done using analog methods were still in vogue – collages and photomontages used to get ideas across to clients. A decade later, personal computers came along, and that saw the emergence of CAD and the wider adoption of digital rendering. Today’s architectural renderings are almost imperceptible from reality, with the increase in sophistication of rendering sofware. We struggle to tell the difference between what is a rendering and what is not – or rather we ....

Matthew Maganga , Masahiro Mori , Alexis Christodoulou , Architectural Rendering , Slippery Slope , Uncanny Valley , Articlenot Experienced , Can Use , Fala Atelier , Augmented Reality , Digital Architecture , 3d Rendering , Most Digital , Archdaily Topic 2021 Rendering , Uncanny Valley , மசாஹிரோ மோரி , கட்டடக்கலை ரெண்டரிங் , வழுக்கும் சாய்வு , விசித்திரமான பள்ளத்தாக்கு , முடியும் பயன்பாடு , ஃபாலா அடேலீயர் , பெரிதாக்கப்பட்டது ரியாலிடீ ,

"Relation Regularized Scene Graph Generation" by Yuyu Guo, Lianli Gao et al.

Scene graph generation (SGG) is built on top of detected objects to predict object pairwise visual relations for describing the image content abstraction. Existing works have revealed that if the links between objects are given as prior knowledge, the performance of SGG is significantly improved. Inspired by this observation, in this article, we propose a relation regularized network (R2-Net), which can predict whether there is a relationship between two objects and encode this relation into object feature refinement and better SGG. Specifically, we first construct an affinity matrix among detected objects to represent the probability of a relationship between two objects. Graph convolution networks (GCNs) over this relation affinity matrix are then used as object encoders, producing relation-regularized representations of objects. With these relation-regularized features, our R2-Net can effectively refine object labels and generate scene graphs. Extensive experiments are conducted on ....

Feature Extraction , Raph Convolution Networks Gcns , Cene Graph Generation Sgg , Task Analysis , Isual Relationship , கேளுங்கள் பகுப்பாய்வு ,

Slingshot Aerospace Selected By U.S. Air Force For $1.2M Contract


socaltech.com
El Segundo, California- and Austin, Texas-based
Slingshot Aerospace said on Tuesday that it has scored a contract from the
U.S. Air Force, to develop a next generation tool for the military to streamline visualization of enemy missiles. According to Slingshot Aerospace, the $1.2M contract came in partnership with Culver City, CA-based
Zoic Labs, a software and advanced visualization company, which is related to visual effects company Zoic Studios. Zoic Studios apparently created the visual effects for Avengers: Age of Ultron. The two said they will partner to generate video streams from complex data using Hollywood visual effects best practices. ....

United States , Culver City , El Segundo , Slingshot Aerospace , Slingshot Aerospace Selected By Us Air , Us Air , Force For , Texas Based Slingshot Aerospace , Zoic Labs , Zoic Studios , ஒன்றுபட்டது மாநிலங்களில் , கல்கர் நகரம் , எல் செகுண்டோ , ஸ்லிங்ஷாட் விண்வெளி , எங்களுக்கு அேக , படை க்கு , டெக்சாஸ் அடிப்படையிலானது ஸ்லிங்ஷாட் விண்வெளி , ஜோய்க் ஆய்வகங்கள் , ஜோய்க் ஸ்டுடியோக்கள் ,