Jennifer Furrer’s Post

In 2008, Radiohead released House of Cards — one of the first mainstream music videos created entirely from 3D scan data using LiDAR and structured light. What made it even more remarkable was that they released the raw 3D data publicly, inviting developers, artists, and technologists to experiment with, remix, and reinterpret it. It was an early open-data moment for spatial media. Since then, more than a dozen volumetric capture technologies have evolved — from depth cameras and photogrammetry to LiDAR, industrial CT, NeRFs, Gaussian splats, and volumetric video stages. The Voxon Photonics 3D Volumetric VLED Platform enables that volumetric data to be replayed in the same form in which it was captured — no glasses, no headsets, unlimited viewing angles, and every point of light emitted from a unique position in space. It’s data you can walk around and view from any direction, even from above. To find out more, please visit www.voxon.co or email contact@voxon.co . The 3D data used is available here: https://lnkd.in/g6YyTwUX

Collaboration turns geospatial data into a competitive advantage 🗺️ Woolpert’s AI team, Atlas Labs, has partnered with Rivers Agile to transform its hyper-dense aerial lidar and elevation data into immersive 3D simulations using NVIDIA Omniverse. The partnership’s result is real-time digital twins that strengthen infrastructure monitoring, accelerate environmental response planning, and deliver sharper operational visibility to architectural and engineering leaders where it matters most. 📖 Access the full press release here: https://buff.ly/rn5aMiO Pittsburgh Robotics Network | Innovation Works | Robotics Factory | Allegheny Conference on Community Development | Pittsburgh Region. Next is Now. | Southwestern Pennsylvania Commission | Pennsylvania Department of Community & Economic Development | New Economy Collaborative | Allegheny County Economic Development

Metaroom by Amrax x Siemens at Light + Building 2026 March 8–13 | Hall 11.0, Stand B56 As a partner in the Siemens Xcelerator Marketplace, Metaroom integrates mobile LiDAR-based site capture into the Building X ecosystem. The challenge Most existing buildings lack the structured spatial data required for digital lifecycle workflows. The solution Metaroom captures true-to-scale room geometry on-site and generates: - Structured 3D building data - IFC-ready spatial models - Accurate 2D floor plans This data feeds directly into the Building X ecosystem — enabling: - Building operations - Structured space documentation - Ongoing facility and asset workflows Instead of complex and costly terrestrial laser scanning, use scalable mobile LiDAR capture to digitize existing building stock efficiently. See the live integration at the Siemens booth in Hall 11.0, Stand B56. For a deeper discussion, visit our main booth in Hall 8.0, Booth F83.

Metaroom by Amrax x Siemens at Light + Building 2026 Hall 11.0, Stand B56 As a partner in the Siemens Xcelerator Marketplace, Metaroom integrates mobile LiDAR-based site capture into the Building X ecosystem. The challenge Most existing buildings lack structured spatial data required for digital lifecycle workflows. The solution Metaroom captures true-to-scale room geometry on-site and generates: Structured 3D building data IFC-ready spatial models Accurate 2D floor plans This data feeds directly into the Building X ecosystem — enabling: Building operations Structured space documentation Ongoing facility and asset workflows Instead of complex and costly terrestrial laser scanning, use scalable mobile LiDAR capture to digitize existing building stock efficiently. See the live integration at the Siemens booth in Hall 11.0, Stand B56. For a deeper discussion, visit our main booth in Hall 8.0, Booth F83.

The End of 2D Broadcasting: Volumetric Optics 🏀 Current sports broadcasting relies on 2D pixel arrays, giving us a "flat" representation of a three-dimensional world. The first pillar of what I call the "Expansion" changes this completely by shifting to Volumetric Optics. In this new model, the camera evolves from a passive lens into an active LIDAR and spatial computing sensor array. It is designed to capture not just light, but vectors, velocity, and mass. Watch the video one here. When that basketball player leaps, the system isn't recording a traditional video of the jump; it records the exact physics of the movement: Vector v: The direction and speed. Mass m: The force exerted on the floor. Spatial Coordinates (x, y, z): The exact volumetric displacement of air. This data isn't meant for a standard screen. It is meant for a Physical Physics Engine. Part 2 is coming soon, where we explore the Faster-Than-Light (FTL) transmission of this physics blueprint straight to a robotic receiver. #SpatialComputing #FutureOfMedia #VolumetricOptics #SportsTech #ArtificialIntelligence #PhysicsEngine #NextGenMedia

New Post: Real‑Time Multimodal Sensor Fusion for Adaptive Synesthetic Interactive Sculpture Systems - — ### Abstract This paper presents a complete end‑to‑end framework for adaptive synesthetic interactive sculpture systems \(SISS\) that combine visual, haptic, and acoustic streams into a coherent, immersive experience. Leveraging depth‑camera bootstrapping, LIDAR‑based spatial modeling, and data‑driven audio‑visual mapping, we demonstrate a 70 % reduction in latency compared to conventional plug‑and‑play setups while maintaining 99.5 % frame‑rate \[…\]

Fidelity without accuracy is just a rendering; true digital twins require both. For studio leads and technical directors building operational digital replicas, relying on isolated photogrammetry or laser scanning is insufficient. Combining LiDAR frameworks with volumetric capture establishes the necessary data density for real-time engines to function as absolute simulation environments. Key Takeaways: * Structural vs. Surface Data: LiDAR dictates millimeter-accurate architectural geometry, while volumetric pipelines supply high-fidelity material and textural nuance. * Pipeline Convergence: Integrating dense point-cloud arrays and volumetric meshes into a unified real-time engine necessitates stringent data optimization to maintain operational frame rates. * Functional Integrity: This integrated capture method transitions digital twins from visual novelties to reliable utility models capable of supporting complex spatial analysis and precise VFX pre-visualization. #DigitalInfrastructure #LiDAR #RealTimePipelines #TheVoltas #DigitalTwin Read the full journal here:

The Way We Capture Reality Is Changing — And LIXEL K1 Is Part of That Shift A few years ago, capturing a full construction site or industrial facility meant heavy equipment, long setup times, and waiting days (sometimes weeks) before you could actually use the data. Today, tools like XGRIDS LIXEL K1 are changing that reality. I’ve been closely following how mobile LiDAR systems are evolving, and what stands out about LIXEL K1 is not just the technology — it’s the practicality. You walk the site. It captures in real time. You leave with structured spatial data. No complicated static setups. No endless repositioning. No guessing what you might have missed. What makes LIXEL K1 interesting? From what XGRIDS highlights, it combines: • High-performance LiDAR for dense, accurate point clouds • Integrated imaging for visual context • SLAM-based real-time mapping • A compact, field-ready form factor • Faster field-to-office workflows In short — it’s built for people who actually work on-site. Where does this really make a difference? 🏗 Construction teams using it for progress tracking and as-built verification ⛏ Mining operators capturing pits and tunnels safely and efficiently 🏭 Industrial facilities mapping complex layouts before upgrades 🎬 Film & virtual production teams digitizing real environments for XR workflows What I find most interesting is how this supports the shift toward digital twins and smarter planning. The faster you capture reality, the faster you can make decisions. And in most projects, speed directly affects cost. We’re moving from: “Let’s document this site” to “Let’s turn this site into actionable insight.” That’s a big shift. I’m curious — If you work in construction, infrastructure, mining, or industrial facilities: 👉 Where do you see mobile LiDAR having the biggest impact in your workflow? Let’s discuss. #XGRIDS #LIXELK1 #RealityCapture #LiDAR #3DScanning #DigitalTwin #ConstructionTech #MiningTech #IndustrialInnovation

The human eye and LiDAR share the same limitation: line of sight. But to truly understand an existing or planned building, it helps to go beyond the line of sight. In point clouds, many of you already use NUBIGON’s signature X-ray display to reveal what’s hidden behind surfaces. What’s less known: you can apply the same idea to CAD/BIM mesh models. Simply select the mesh, set Alpha Mode → Blend, and adjust the alpha factor to get the desired level of transparency. Now the model no longer blocks the view. You can see the context while still keeping the design visible. A simple trick that makes coordination, validation, and presentations much clearer.

𝗙𝗿𝗼𝗺 𝗥𝗲𝗮𝗹 𝗧𝗲𝗿𝗿𝗮𝗶𝗻 𝘁𝗼 𝗮 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁 𝗶𝗻 𝟱 𝗛𝗼𝘂𝗿𝘀 Most simulation environments are not built from real terrain. They are usually manually modeled from DEMs, orthophotos, maps, or simplified 3D assets. As a result, many simulations operate on terrain that approximates reality rather than reproducing it. So, what happens when the environment is generated directly from the terrain itself? This animation shows a digital twin generated from aerial photogrammetry in the Tagus River valley between the Castillo de Oreja and the Despoblado de Oreja in Toledo, Spain. The goal of this experiment was simple. Measure how quickly a real landscape can be converted into a simulation ready virtual environment. The workflow combines aerial capture using the drone DJI Matrice 4E and photogrammetric reconstruction optimized for real time simulation environments such as Unreal Engine. Key metrics from this test area • Surface digitized 5 hectares • Images captured 3,500 • Flight altitude approximately 40 m • Flight operations about 1.5 batteries • Total flight time 1 hour 15 minutes • Original reconstruction 266M triangles • Simulation model 300K triangles • Photogrammetric reconstruction time 3 hours • Processing workstation RTX 5080 GPU and Intel i9 CPU • Environment preparation for simulation 1 hour • Total pipeline time from capture to simulation environment about 5 hours The key point is not just terrain mapping but how quickly real locations can become operational digital environments for simulation, training, or territorial analysis. Possible applications include simulation of mobility in complex terrain, virtual training environments derived from real landscapes, testing and validation of autonomous systems, emergency planning, and infrastructure security analysis. Traditional comparison Simulation environments are often constructed manually from heterogeneous geospatial datasets and manual terrain modeling. Even for relatively small areas this process can take several days depending on the required detail. In this experiment the terrain geometry is derived directly from aerial photogrammetry, allowing a real environment to become simulation ready in approximately five hours. Scalability This test focuses on a 5-hectare terrain segment to measure pipeline speed. However, the same workflow scales to larger areas. Dronícola has previously processed aerial photogrammetry datasets covering up to approximately 200 hectares of terrain. Larger areas increase image counts and flight missions, while reconstruction can be divided into spatial tiles and optimized into modular terrain segments for real time simulation. If generating simulation environments directly from real terrain is becoming this fast, should manual terrain modeling still be the default approach? #DigitalTwins #Photogrammetry #Simulation #DroneMapping #Geomatics