Die Illuminating Engineering Society (IES) setzt sich für die Verbesserung unserer beleuchteten Umwelt ein. Dafür vereint sie Menschen mit Fachwissen rund um Licht und lässt dieses Wissen in konkrete Maßnahmen zum Wohle der Allgemeinheit einfließen.

Nahezu 50 technische Komitees, besetzt mit über 1.000 freiwilligen Experten, bringen ihre Kompetenz ein. Als akkreditierter Entwickler von Standards des American National Standards Institute (ANSI) sorgt die IES für Stabilität und Verlässlichkeit im Lichtmarkt – zugunsten der Industrie und der Öffentlichkeit.

Ein strenger Konsensfindungsprozess mit mehreren Ebenen der Prüfung und öffentlicher Beteiligung garantiert die Unparteilichkeit und Praxisnähe der erarbeiteten Richtlinien und Ressourcen. Diese sind zusammengefasst in der sogenannten IES Lighting Library, die fünf Sammlungen umfasst.

Kernpunkte:
Ziel: Verbesserung der beleuchteten Umwelt
Mittel: Bündelung von Lichtwissen und Umsetzung in praktische Maßnahmen
Träger: Freiwillige Experten in technischen Komitees
Ergebnisse: Stabilität und Verlässlichkeit im Lichtmarkt durch ANSI-akkreditierte Standards
Umsetzung: Strenger Konsensfindungsprozess für die IES Lighting Library mit fünf Sammlungen praktischer Richtlinien

Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗’s Stable Diffusion with 🧨Diffusers blog.

Model Details

• Developed by: Robin Rombach, Patrick Esser
• Model type: Diffusion-based text-to-image generation model
• Language(s): English
• License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based.
• Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (CLIP ViT-L/14) as suggested in the Imagen paper.
• Resources for more information: GitHub Repository, Paper.

https://huggingface.co/CompVis/stable-diffusion-v1-4

Stable Diffusion v2 Model Card

This model card focuses on the model associated with the Stable Diffusion v2 model, available here.

This stable-diffusion-2 model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on 768x768 images.

https://huggingface.co/stabilityai/stable-diffusion-2

Stable Diffusion x4 upscaler model card

This model card focuses on the model associated with the Stable Diffusion Upscaler, available here. This model is trained for 1.25M steps on a 10M subset of LAION containing images >2048x2048. The model was trained on crops of size 512x512 and is a text-guided latent upscaling diffusion model. In addition to the textual input, it receives a noise_level as an input parameter, which can be used to add noise to the low-resolution input according to a predefined diffusion schedule.

https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler

https://docs.opencv.org/3.4/dd/d53/tutorial_py_depthmap.html

ABSTRACT:
RGB-D cameras are novel sensing systems that can rapidly provide accurate depth information for 3D perception, among which the
type based on active stereo vision has been widely used. However, there are some problems exiting in use, such as the short
measurement range and incomplete depth maps. This paper presents a robust and efficient matching algorithm based on semi-global
matching to obtain more complete and accurate depth maps in real time. Considering characteristics of captured infrared speckle images,
the Gaussian filter is performed firstly to restrain noise and enhance the relativity. It also adopts the idea of block matching for reliability,
and a dynamic threshold selection of the block size is used to adapt to various situation. Moreover, several optimizations are applied
to improve precision and reduce error. Through experiments on the Intel Realsense R200, the excellent capability of our proposed
method is verified.

https://isprs-annals.copernicus.org/articles/V-2-2021/107/2021/isprs-annals-V-2-2021-107-2021.pdf

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Projectors for D400 Series Depth Cameras

In this white-paper we explore the questions of when, where, and why the Intel RealSense™ D4xx series of stereo-depth cameras need to use an optical projector, and more importantly, what performance benefits can be derived from optimizing the projector further or using external projectors.

https://dev.intelrealsense.com/docs/projectors

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Industrial High-Power Flood Illuminator

Abstract
The EGA2000-940-UW is a small form factor surface mount infrared laser illuminator enabling a
variety of use cases for industrial mass market applications

https://www.mouser.de/datasheet/2/588/EGA2000_940_UW_Short_Datasheet_DS001015_2_00-2326921.pdf

http://graphics.stanford.edu/papers/fasticp/fasticp_paper.pdf

„The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial
estimate of the relative pose is known. Many variants of ICP have
been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy. We
enumerate and classify many of these variants, and evaluate their
effect on the speed with which the correct alignment is reached.
In order to improve convergence for nearly-flat meshes with small
features, such as inscribed surfaces, we introduce a new variant
based on uniform sampling of the space of normals. We conclude
by proposing a combination of ICP variants optimized for high
speed. We demonstrate an implementation that is able to align
two range images in a few tens of milliseconds, assuming a good
initial guess. This capability has potential application to real-time
3D model acquisition and model-based tracking.“

http://graphics.stanford.edu/papers/fasticp/fasticp_paper.pdf

http://www.humus.name/Articles/Persson_LowLevelThinking.pdf

from Emil Persson

Quick Introduction to Mesh Shaders (OpenGL and Vulkan)

Blue Screen Matting

https://graphics.stanford.edu/courses/cs148-09-fall/papers/smith-blinn.pdf

Basic Matting

http://vision.cse.psu.edu/courses/CompPhoto/basicmatting.pdf

Digital Matting

https://grail.cs.washington.edu/projects/digital-matting/papers/cvpr2001.pdf

Natural Image Matting

https://openaccess.thecvf.com/content_CVPR_2019/papers/Tang_Learning-Based_Sampling_for_Natural_Image_Matting_CVPR_2019_paper.pdf

https://github.com/avatsaev/webb-tracker-api

James Webb Telescope tracking REST API

Public REST API to track JWST’s current status

API data source: https://www.jwst.nasa.gov/content/webbLaunch/whereIsWebb.html

Instant configuration of NDI® on NETGEAR’s popular M4250 AV Line of switches through a unique NDI configuration profile.

NETGEAR has been making a name for itself for years with switches that are engineered for AV over IP. Unique features built into all NETGEAR Pro AV switches like NETGEAR IGMP Plus™, Auto-Trunk and Auto-LAG follow IT best practices to ensure that any NDI AV over IP deployments will work with minimal configuration required.

Read the full article on: https://www.netgear.com/blog/business/best-switches-for-ndi-network-streaming/

…“To avoid this long scattershot process of locating bugs, fixing them and checking they don’t reoccur, we need to regularly check that this issue isn’t in the current version of the game.-For this AI example, we could get a tester to run a scenario like the one in the video everyday, getting a skeleton to chase them around a corner and checking that it remembers to follow them. That wouldn’t take too long, but if we wanted checks for a whole game of the size of Sea of Thieves we’d have thousands of these scenarios that needed testing. Now these checks would take so much time that they would become a manual tester’s entire job, and a pretty mind-numbingly boring one at that.-If instead of wasting a human’s time with these checks, we let the game do it itself via an automated test, we not only avoid wasting a tester’s time but we get many other advantages. -First the game can run the checks much faster than a human could, which means they can be run more often so the dev team will know even sooner that there’s a problem. Next, the game can be more precise when running tests as its able to read and check its own state, so more accurate checks can be made than a human could detect. Finally, automated tests can also interact with the game at different levels, testing individual code functions if necessary, while a human can only play the game in its complete form.“…

Read the full article: https://ubm-twvideo01.s3.amazonaws.com/o1/vault/gdc2019/presentations/Masella_Robert_AutomatedTestingOf.pdf