What you see is 3DGS. What you hit is LiDAR. At Greneta Corp., we turned a real industrial site into a Sim-Ready environment for Physical AI. The environment combines: • 3D Gaussian Splatting (3DGS) for photorealistic visual representation • LiDAR-based volumetric octree mapping for spatial geometry and depth sensing • Collision geometry for physical interaction A Unitree Robotics H1 can now walk through the reconstructed site in Nvidia Isaac Sim — seeing through RGB, sensing depth against the volumetric map, and physically interacting with the environment. Real → Reconstruction → Sim-Ready → Robot-Ready. Beyond digital twins — training grounds for Physical AI. P.S. For this demo, we intentionally configured a forward-facing camera and a rear-facing LiDAR. In NVIDIA Isaac Sim, sensor placement and specifications can be freely configured for different simulation scenarios. #Greneta #PhysicalAI #SimReady #RealToSim #3DGS #GaussianSplatting #IsaacSim #NVIDIA #Robotics #LiDAR #DigitalTwin | 13 comments on LinkedIn

Kotaku just posted:Get Off My Screen Dork, I’m Trying To PlayStar Wars: Zero CompanyOne Ovissian is too many in my book...https://kotaku.com/get-off-my-screen-dork-im-trying-to-playstar-wars-zero-company-2000730324#gamingNews #Kotaku

My name is Asmaa. I am 23 years old, and I am from Gaza.

Contribute to amine0110/vedo-tutorials development by creating an account on GitHub.

Contribute to amine0110/vedo-tutorials development by creating an account on GitHub.

How can an AI partner stay legible and negotiable when the primary interface is the body — not the text box?

Empower your frontline with hands-free, AI-powered error detection, procedural guidance, and QA support delivered through smart glasses.

What would it take to bring Mesa’s open source Vulkan driver for AMD GPUs to Windows? RADV’s experimental Windows port is getting closer to running real workloads, while highlighting one of the key challenges ahead: building a stable interface to AMD’s proprietary kernel driver. https://www.collabora.com/news-and-blog/news-and-events/cracking-windows-open-porting-radv-to-win32.html @vulkan@fosstodon.org #RADV #Mesa3D #OpenSourceGraphics #GPUDrivers #OpenSource

Stepping back from FreeCAD maintaining - Yorik's blog https://yorik.uncreated.net/?blog%2F2026%2F2026-002-stepping-out-of-freecad-maintaining

# Perimetric coordinate system : Eccentricity and Half-meridian Figure 1: Static 3D representation of the perimetric coordinate system. The PTVR **perimetric** coordinate system is a **spherical** coordinate system that is used extensively by vision scientists. In this coordinate system, the **3D position of a point P** is defined by 3 coordinates: – The **Half-meridian**(from 0° to 360°). – The **Eccentricity** (from 0° to 180°). – The **Radial Distance** (in meters) between Origin and P. The 3 coordinates of point P in Figure 1 are: Half-meridian: 135° Eccentricity: 20° Radial distance: any distance in meters. Figure 2: Horizontal rotation (i.e. about the Y axis) of the **perimetric** coordinate system shown in figure 1 A horizontal rotation (i.e. about the Y axis) of the **perimetric** coordinate system shown in figure 1 is shown in Figure 2. This rotation aims at providing a better mental image of 3D space. Figure 3: **Eccentricity** of point P varies from 0° to 40° while keeping Half-meridian constant at 135°. | Figure 4: **Half-meridian** of point P varies from 0° to 360° while keeping eccentricity constant at 20°. ---|--- # Flat Tangent Screen

# Perception Toolbox for Virtual Reality PTVR is a free and open-source library for creating visual perception experiments in virtual reality using high-level Python script programming. It aims at helping behavioral science researchers leverage virtual reality’s power for their research without the need to learn how virtual reality programming works. The philosophy of PTVR is thus very close to the approach of PsychoPy that has been so important and influential since 2007 for Vision Science researchers displaying stimuli on 2D monitors. --- #### Some of PTVR assets are: * Researchers can create VR experiments rapidly and succinctly thanks to clearly structured high-level python scripts. * **Serious Games** with sophisticated interactivity (pointing with handset, headset or gaze) can also be created in the same way. * The use of such scripts, along with the transparency of the PTVR open-source code, is one of the necessary components to address the ‘reproducibility crisis’ in science and foster **Open Science**. * Researchers can learn PTVR quite quickly thanks to two primary resources: * The rich online documentation with many **animated** figures in 3D to visualize 3D subtleties. * Demo scripts with didactic 3D and interactive features. * The **terminology** used in the PTVR code is familiar to vision scientists. For instance, researchers can use an intuitive “**Perimetric** ” coordinate system, allowing them to place their 3D stimuli with eccentricity and half-meridian coordinates easily. * Intuitive ways of dealing with **visual angles** in 3D. * Intuitive ways of dealing with the notorious difficulty of **rotating 3D objects**. * An implementation of “**flat screens** ” to replicate and extend standard experiments made on 2D screens of monitors. * Easy ways of **recording** experimental results. * Easy **Gaze** tracking and recording (only with HTC Vive pro Eye – in May 2025). * Easy **Head** tracking and recording. * Focus on implementing the standard and innovative **clinical tools** for visuomotor testing and visuomotor readaptation (notably for **low vision**). * Great care to create accurately controlled **text stimuli** in the spirit of the psychophysics of Reading. #### Requirements * An HTC Vive Pro Series VR system. The HTC Vive Pro EYE includes an eyetracker. * Or A QUEST 1, 2 or 3 system (starting from PTVR release 2.0.0 in May 2025) – no eyetracking. * A Windows computer with VR system already set-up. * Python >3.6. We recommend Spyder, a nice integrated development environment (IDE). #### Notes * Future PTVR versions will rely more and more on OpenXR to ensure as much as possible that PTVR will behave similarly on all conformant devices. * PTVR is built using the Unity game engine as a backend (you will however not have to install Unity yourself).

As the organizer of this campaign, I am reaching out on behalf of Mohamed, a remarkable 15-year-old boy from Gaza whose childhood has been overshadowed by hardship, loss, and responsibility far beyond his years.

80 Level just posted: Full-Body, Hand & Facial Motion Capture From Any Video In Blender BlendCap is completely local and offline. https://80.lv/articles/full-body-hand-facial-motion-capture-from-any-video-in-blender/ #gamingNews #80Level

# Perception Toolbox for Virtual Reality PTVR is a free and open-source library for creating visual perception experiments in virtual reality using high-level Python script programming. It aims at helping behavioral science researchers leverage virtual reality’s power for their research without the need to learn how virtual reality programming works. The philosophy of PTVR is thus very close to the approach of PsychoPy that has been so important and influential since 2007 for Vision Science researchers displaying stimuli on 2D monitors. --- #### Some of PTVR assets are: * Researchers can create VR experiments rapidly and succinctly thanks to clearly structured high-level python scripts. * **Serious Games** with sophisticated interactivity (pointing with handset, headset or gaze) can also be created in the same way. * The use of such scripts, along with the transparency of the PTVR open-source code, is one of the necessary components to address the ‘reproducibility crisis’ in science and foster **Open Science**. * Researchers can learn PTVR quite quickly thanks to two primary resources: * The rich online documentation with many **animated** figures in 3D to visualize 3D subtleties. * Demo scripts with didactic 3D and interactive features. * The **terminology** used in the PTVR code is familiar to vision scientists. For instance, researchers can use an intuitive “**Perimetric** ” coordinate system, allowing them to place their 3D stimuli with eccentricity and half-meridian coordinates easily. * Intuitive ways of dealing with **visual angles** in 3D. * Intuitive ways of dealing with the notorious difficulty of **rotating 3D objects**. * An implementation of “**flat screens** ” to replicate and extend standard experiments made on 2D screens of monitors. * Easy ways of **recording** experimental results. * Easy **Gaze** tracking and recording (only with HTC Vive pro Eye – in May 2025). * Easy **Head** tracking and recording. * Focus on implementing the standard and innovative **clinical tools** for visuomotor testing and visuomotor readaptation (notably for **low vision**). * Great care to create accurately controlled **text stimuli** in the spirit of the psychophysics of Reading. #### Requirements * An HTC Vive Pro Series VR system. The HTC Vive Pro EYE includes an eyetracker. * Or A QUEST 1, 2 or 3 system (starting from PTVR release 2.0.0 in May 2025) – no eyetracking. * A Windows computer with VR system already set-up. * Python >3.6. We recommend Spyder, a nice integrated development environment (IDE). #### Notes * Future PTVR versions will rely more and more on OpenXR to ensure as much as possible that PTVR will behave similarly on all conformant devices. * PTVR is built using the Unity game engine as a backend (you will however not have to install Unity yourself).

Start by installing the necessary libraries. You will need to have Python and pip (the package installer for Python) installed on your…

## Python Libraries for GIS and Mapping Python libraries are the ultimate extension in GIS because they allow you to boost its core functionality. By using Python libraries, you can break out of the mold that is GIS and dive into some serious data science. There are 200+ standard libraries in Python. But there are thousands of third-party libraries too. So, it’s endless how far you can take it. Today, it’s all about Python libraries in GIS. Specifically, what are the most popular Python packages that GIS professionals use today? Let’s get started. **READ MORE:** GIS Programming Tutorials: Learn How to Code ## First, why even use Python libraries for GIS? Have you ever noticed how GIS is missing that one capability you need it to do? Because no GIS software can do it all, Python libraries can add that extra functionality you need. Put simply, a Python library is code someone else has written to make life easier for the rest of us. Developers have written open libraries for machine learning, reporting, graphing, and almost everything in Python. If you want this extra functionality, you can leverage those libraries by importing them into your Python script. From here, you can call functions that aren’t natively part of your core GIS software. **Get to know Python programming:** ###### GIS Programming Tutorials: Learn How to Code ###### GIS Data Engineering: Begin Your ETL Journey ## Python Libraries for GIS If you’re going to build an all-star team for GIS Python libraries, this would be it. They all help you go beyond the typical managing, analyzing, and visualizing of spatial data. That is the true definition of a Geographic Information System. **PRO TIP:** Use pip to install and manage your packages in Python ### 1. Arcpy If you use Esri ArcGIS, then you’re probably familiar with the ArcPy library. ArcPy is meant for geoprocessing operations. But it’s not only for spatial analysis, it’s also for data conversion, management, and map production with Esri ArcGIS. ### 2. Geopandas Geopandas is like pandas meet GIS. But instead of straightforward tabular analysis, the Geopandas library adds a geographic component. For overlay operations, Geopandas uses Fiona and Shapely, which are Python libraries of their own. ### 3. GDAL/OGR The GDAL/OGR library is used for translating between GIS formats and extensions. QGIS, ArcGIS, ERDAS, ENVI, GRASS GIS and almost all GIS software use it for translation in some way. At this time, GDAL/OGR supports 97 vector and 162 raster drivers. ### 4. RSGISLib The RSGISLib library is a set of remote sensing tools for raster processing and analysis. To name a few, it classifies, filters, and performs statistics on imagery. My personal favorite is the module for object-based segmentation and classification (GEOBIA). ### 5. PyProj The main purpose of the PyProj library is how it works with spatial referencing systems. It can project and transform coordinates with a range of geographic reference systems. PyProj can also perform geodetic calculations and distances for any given datum. ## Python Libraries for Data Science Data science extracts insights from data. It takes data and tries to make sense of it, such as by plotting it graphically or using machine learning. This list of Python libraries can do exactly this for you. ### 6. NumPy Numerical Python (NumPy library) takes your attribute table and puts it in a structured array. Once it’s in a structured array, it’s much faster for any scientific computing. One of the best things about it is how you can work with other Python libraries like SciPy for heavy statistical operations. ### 7. Matplotlib When you’re working with thousands of data points, sometimes the best thing to do is plot it all out. Enter Matplotlib. Statisticians use the Matplotlib library for visual display. Matplotlib does it all. It plots graphs, charts, and maps. Even with big data, it’s decent at crunching numbers. ### 8. Pandas The Pandas library is immensely popular for data wrangling. It’s not only for statisticians. But it’s incredibly useful in GIS too. Computational performance is key for pandas. The success of Pandas lies in its data frame. Data frames are optimized to work with big data. They’re optimized to such a point that it’s something that Microsoft Excel wouldn’t even be able to handle. ### 9. Re (regular expressions) Regular expressions (Re) are the ultimate filtering tool. When there’s a specific string you want to hunt down in a table, this is your go-to library. But you can take it a bit further like detecting, extracting, and replacing with pattern matching. ### 10. ipyleaflet If you want to create interactive maps, ipyleaflet is a fusion of Jupyter notebook and Leaflet. You can control an assortment of customizations like loading basemaps, geojson, and widgets. It also gives a wide range of map types to pick from including choropleth, velocity data, and side-by-side views. ### 11. ReportLab ReportLab is one of the most satisfying libraries on this list. I say this because GIS often lacks sufficient reporting capabilities. Especially, if you want to create a report template, this is a fabulous option. I don’t know why the ReportLab library falls a bit off the radar because it shouldn’t. ### 12. Folium Just like ipyleaflet, Folium allows you to leverage Leaflet to build interactive web maps. It gives you the power to manipulate your data in Python, then you can visualize it with the leading open-source JavaScript library. ### 13. Geemap Geemap is intended more for science and data analysis using Google Earth Engine (GEE). Although anyone can use this Python library, scientists and researchers specifically use it to explore the multi-petabyte catalog of satellite imagery in GEE for their specific applications and uses with remote sensing data. **Build your GIS programming toolkit:** ###### ArcGIS API for Python ###### PyQGIS: How To Build a Python Script in QGIS ### 14. LiDAR Simply named the LiDAR Python Package, the purpose is to process and visualize Light Detection and Ranging (LiDAR) data. For example, it includes tools to smooth, filter, and extract topological properties from digital elevation models (DEMs) data. Although I don’t see integration with raw LAS files, it serves its purpose for terrain and hydrological analysis. ### 15. Scikit Lately, machine learning has been all the buzz. And with good reason. Scikit is a Python library that enables machine learning. It’s built into NumPy, SciPy, and Matplotlib. So, if you want to do any data mining, classification or ML prediction, the Scikit library is a decent choice. ## The Python Libraries All-Star Team These are the Python libraries we thought were stand-outs for GIS and data science. Now, it’s time to turn it on to you. If you could build an all-star team of Python libraries, who would you put on your team? Please let us know with a comment below. **PRO TIP:** If you need a quick and dirty list of functions for Python libraries, check out DataCamp’s Cheat Sheets. Subscribe to our newsletter: ##### Digitizing Tips: 15 Must-Know Editing Tools in ArcGIS Pro ##### 20 ArcGIS Pro Tips and Tricks ##### The Periodic Table for Spatial Analysis ##### 35 Differences Between ArcGIS Pro and QGIS 3 ##### 25 Must-Know GIS Editing Tools [Cheatsheet] ##### 15 Python Libraries for GIS and Mapping ##### The Power of Spatial Analysis: Patterns in Geography ##### 7 Geoprocessing Tools Every GIS Analyst Should Know

Kepler.gl is a powerful open source geospatial analysis tool for large-scale data sets. - keplergl/kepler.gl

Visualizing geospatial data using kepler.gl for jupyter notebooks. In this article we'll learn about kepler.gl and use it for visualizing geospatial data.

Best Libraries for Geospatial Data Visualisation in Python | Towards Data Science https://towardsdatascience.com/best-libraries-for-geospatial-data-visualisation-in-python-d23834173b35/

12 Python Libraries for Geospatial Data Analysis | Geoapify https://www.geoapify.com/python-geospatial-data-analysis/

15 Python Libraries for GIS and Mapping - GIS Geography https://gisgeography.com/python-libraries-gis-mapping/

scikit-learn: machine learning in Python — scikit-learn 1.9.0 documentation https://scikit-learn.org/stable/index.html

Best Libraries for Geospatial Data Visualisation in Python https://readmedium.com/best-libraries-for-geospatial-data-visualisation-in-python-d23834173b35

kepler.gl https://kepler.gl/

GitHub - keplergl/kepler.gl: Kepler.gl is a powerful open source geospatial analysis tool for large-scale data sets. · GitHub https://github.com/keplergl/kepler.gl

kepler.gl/docs/user-guides/c-types-of-layers/p-3d-tile-layer.md at master · keplergl/kepler.gl · GitHub https://github.com/keplergl/kepler.gl/blob/master/docs/user-guides/c-types-of-layers/p-3d-tile-layer.md

Kepler.gl https://harshithsai91.github.io/kepler/ "Kepler.gl is a WebGL-powered, open-source tool for displaying massive amounts of geographical data."

Learn how to Visualize Geospatial Data using kepler.gl https://www.analyticsvidhya.com/blog/2020/06/learn-visualize-geospatial-data-jupyter-kepler/

The ArcGIS Pro Python reference contains detailed information about every ArcPy module, function, and class provided with ArcGIS Pro; working with Python; and working with, and creating your own, geoprocessing tools in Python.

The ArcGIS Pro geoprocessing tool reference contains detailed information about every geoprocessing tool provided with ArcGIS Pro.