Realistic Visualization Technology

What if you could fly through someone’s brain — and actually watch it think in real time? 🧠 This stunning 3D visualization makes that possible. It shows live brain activity mapped from EEG (electroencephalography) signals onto a realistic 3D model of the human brain. Each color represents a different brainwave frequency — from calm alpha and focused beta, to fast, high-energy gamma rhythms. The golden lines trace the brain’s white matter pathways, and the moving light pulses represent information flowing between regions — the brain communicating with itself in real time. How it’s built The process begins with MRI scans to create a high-resolution 3D model of the brain, skull, and scalp. Then, DTI (Diffusion Tensor Imaging) maps the brain’s wiring — the white matter tracts that connect its regions. Next comes EEG recording, captured using a 64-channel mobile EEG cap. Advanced software pipelines like BCILAB and SIFT clean the data, remove noise, and use mathematical modeling to “source-localize” brain activity — estimating where in the brain each signal originates. They also analyze information flow using a technique called Granger causality, revealing which brain regions are influencing others at any given moment. From Data to Experience All of this is brought to life in Unity, a 3D engine usually used for games. Here, the brain becomes a fully navigable world — you can literally fly through it using a controller and watch live signals flicker and flow. It’s data turned into experience — a fusion of neuroscience, art, and technology that lets us see the living mind at work. Why it matters By merging EEG, MRI, and DTI, researchers can study how the brain’s networks communicate, and how this connectivity changes in conditions like epilepsy, depression, or neurodegenerative diseases. This work also pushes forward brain-computer interface research — paving the way for future technologies that help restore movement, communication, or sensation through brain signals alone. Every flicker of light here represents a thought, a signal, a decision — the brain in motion. 🎥 Video Credits: Dr. Gary Hatlen

AI visualization gets real tool for architecture Recently I shared a professionally produced time-lapse of a multi-family building going up. This time, I’m revisiting the idea from a different angle: architect Obid Khikmatullayev sketched the construction stages of a planned building called “Eastern Pearl” in Tashkent — then used AI to turn those sketches into a clean, staged build sequence. In a sea of unrealistic AI videos, this one stands out. It feels authentic, it’s satisfying to watch, and most importantly: it actually served its purpose as a design-communication tool. Key takeaways engineers will appreciate: • AI shines when grounded in real geometry and staged logic — not fantasy. • Rapid visualization compresses hours or days of workflow into minutes. • Lower cost means more iteration, better early-phase coordination. • Clear phasing helps everyone: architects, contractors, clients. I couldn’t find more verified information about the “Eastern Pearl” project, so if you know more, please share — collective knowledge matters in our field. This is the kind of AI use that elevates engineering communication. What examples have you seen where AI actually improved project clarity? 🎥 by obidjon_xikmatullayev

📢 Our lab has been exploring 3D world models for years — and we're thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang: https://lnkd.in/ePg-nUYR PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications, especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, and (3) interactive digital twins.

What happens when you stop treating medical imaging like a *web app*—and start unleashing the full power of the machine? With a native application built to leverage both CPU and GPU, and powered by Apple’s Metal framework, we’re entering a new era of real-time medical visualization. 🚀 Imagine this: A full-body CT scan. 110 high-resolution 3D textures. All rendered in real time. No lag. No compromise. Just fluid, interactive exploration of complex anatomy—instantaneously. This is the advantage of going native: Direct access to hardware → maximum performance GPU acceleration → massive parallel processing Metal optimization → ultra-efficient rendering pipelines Zero abstraction overhead → predictable, consistent speed The result? A transformative user experience where clinicians and researchers can interact with data as fast as they can think. This isn’t just about speed. It’s about unlocking new possibilities in diagnosis, education, and simulation—where precision and immediacy truly matter. The future of medical imaging is not just high-resolution. It’s real-time, immersive, and native. This video is performed on an *old* M1 processor of 2022. #MedicalImaging #GPU #Metal #3DVisualization #HealthTech #Innovation

My master’s thesis was about 3D city modeling & CityGML LOD standards. That was 17 years ago. Back then, it felt like the future. And honestly? It still is. Today, CityGML and LODs became the backbone of city modeling: • metadata-rich • widely adopted • maintenance heavy In the meantime the world didn’t stop. Reality Mapping showed up with: • DSM • True Ortho • 3D meshes • Gaussian Splats Not as a replacement. But as a wake-up call. Visual truth. Context. Speed. Can be combined. Here’s the part many still get wrong 👇 We keep framing this as either / or. That’s a false choice. In real projects, real cities, real Digital Twins: ↳ CityGML (LOD2 / LOD3) provides:  • ontology  • structure  • attributes  • semantics ↳ Reality layers provide:  • realism  • atmosphere  • instant context  • human understanding And when they collaborate in one scene, one project, one application: • DSMs help update and validate city models • True Ortho enables new feature extraction with GeoAI • Meshes / Splats become the visual skin over a metadata-rich CityGML core That’s not redundancy. It’s your leverage. Context + Realism + Ontology Human-readable and machine-actionable. ~ This isn’t about choosing the “next” technology. It’s about learning how to combine the best of both worlds and acting on it, so you’re not left behind. If this resonates, please pass it on ✨

Virtual simulations from game tech will increasingly be used for real-world applications Last week we at Andreessen Horowitz did a round up of the Big Ideas for 2025 Here was mine: Traditionally, games have been virtual world simulations designed for fun. Now gaming technology is extending beyond entertainment to transform how businesses operate. While gaming has long pioneered breakthrough technologies — from Nvidia’s graphics to Unreal Engine’s real-time 3D rendering — these tools are now solving critical business challenges. Consider Applied Intuition, a company built on Unreal Engine, which creates virtual simulations to train and test autonomous vehicles. Three forces are accelerating this shift: generative AI is slashing the cost of virtual content creation; advanced 3D capture technologies are digitizing real-world environments (aka digital twins); and next-generation XR devices are making immersive experiences practical for workers. The applications are already here: Anduril Industries leverages game engines for defense simulations; Tesla  creates virtual worlds for autonomous systems; BMW is incorporating AR in future heads-up display systems; Matterport revolutionizes real estate with virtual walkthroughs; Traverse3D helps companies unlock virtual interactive training for their workforce. Whether it’s training autonomous systems in virtual environments, helping consumers shop with 3D visuals, or scaling tomorrow’s workforce via simulations, I think game tech will infuse every sector in 2025

I've been thinking a lot about what separates basic 3D web viewers from truly enterprise-grade solutions. As more companies invest millions in 3D product creation, it's crucial that the technology used to showcase these assets doesn't become the weakest link. Here are some key considerations that often get overlooked: 🔄 Flexible sharing options - Can you embed it as an iframe? Share with a simple link? Integrate into existing systems? Your viewer should work seamlessly everywhere you need it. ✨ Superior visual quality - Look for advanced rendering capabilities like custom HDRI maps, adjustable tone mapping, and essential post-processing effects. Without proper sharpening filters, materials like cotton simply fall flat. 🔧 Customizable rendering - Your viewer should offer adjustable tone mapping, HDRI maps, and other settings that can be fine-tuned for different product categories. ⚙️ Presets & API access - Critical for scalability! Can you create standardized presets for different product lines (footwear vs. apparel)? Without this, you'll need someone tweaking settings for every single model—a workflow nightmare at scale. 📱 Multi-platform support - Your viewer needs to perform flawlessly across Android, iOS, Vision Pro, and whatever comes next. 3D technology evolves rapidly! 🛠️ Beyond open source - While open source viewers can work initially, many organizations abandon homegrown projects when customization becomes necessary. Without specialized WebGL expertise, simple changes become technical nightmares. ⚡ Optimization built-in - Even the best viewer is useless if models take forever to load. Enterprise solutions must automatically balance file size with visual quality. General-purpose DAM systems weren't built with 3D in mind. When you've invested significantly in 3D assets, doesn't it make sense to ensure they look their absolute best? What other considerations would you add? I'd love to hear your thoughts. Check the comments for the difference between the assets with and without post-processing filters applied. #3DTechnology #ProductVisualization #EnterpriseGrade

The old playbook for launching products online is broken. You no longer need: - Expensive product photoshoots - 3-week turnaround times - Physical prototype shots for every angle - Professional studios for MVP validation AI-powered visualization tools are revolutionizing how we bring products to market online. Both ChatGPT-4o and Google's Gemini (paid versions) can generate photorealistic product renders that convert browsers into buyers. Here's the Prompt Framework: "Photorealistic render of a [object description] on a [base surface], with a [background type]. Style is [aesthetic keywords], featuring [lighting type], [material details], and [design highlights]. Convert sketch to a detailed product visualization." Why This Matters for Online Sellers: - 75% of online shoppers rely on product photos for purchase decisions - Professional product photography costs $75-500 per image - Most products need 5-8 angles minimum - Traditional timeline: 2-4 weeks per product line The AI Alternative: Cost per image: $0 Time per image: 60 seconds Variations possible: Unlimited Consistency: 100% on-brand Real-World Success Stories: 1. Kickstarter Campaign Win: One of our clients launched a modular desk organizer. Instead of manufacturing prototypes: Generated 25 product visualizations using Gemini Created lifestyle shots showing different configurations Raised $140,000 (120% of goal) Total visualization cost: $0 2. Amazon FBA Launch: Entrepreneur tested 10 product variations visually before manufacturing: - Used ChatGPT-4o for initial concepts - Switched to Gemini for fabric/texture details (superior for soft goods) - Identified winning design through A/B testing - Saved $3,000 in sample production 3. Shopify Store Transformation: - Replaced entire product catalog photos (200+ SKUs) - Maintained consistent lighting and style - Conversion rate increased 23% - Photography budget redirected to advertising ChatGPT-4o Excels At: ✓ Hard surface products (electronics, tools, machinery) ✓ Minimalist/modern aesthetics ✓ Technical product details ✓ Consistent studio lighting Gemini Often Better For: ✓ Soft goods (clothing, textiles, furniture) ✓ Natural/organic products ✓ Lifestyle scenes with context ✓ Artistic/creative angles For founders and product developers, this changes everything: → Validate Before You Build: Test market response with photorealistic renders → Iterate at Light Speed: Try 50 variations in an afternoon → Crowdfunding Ready: Launch campaigns without physical prototypes → Investor Presentations: Show vision without manufacturing costs Check out the tips in the attached infographic for generating stunning product shots.

3DGS looks amazing, but it requires millions of tiny blobs to look that good! There has to be a better way... and it turns out, surfels plus some neural magic are coming to the rescue! Check out Nexels, a new representation that decouples how a scene looks from how it's shaped. Instead of using millions of blobs to capture a simple flat wall with a complex texture, it uses a sparse set of quads and a global neural field to handle the heavy lifting. TLDR: Insane Efficiency: It hits the same quality as Gaussian Splatting but uses up to 31x fewer primitives and a fraction of the memory. Speed: It renders at a smooth 50+ FPS, making it twice as fast as previous textured methods. Details: No more "blurry blobs", it uses a technique create sharp edges and flat surfaces that look like the real deal. This is a step toward getting high-fidelity 3D scenes to run on leaner hardware without over optimizing and losing the "wow" factor. Check out the project page for the paper and code: https://lnkd.in/grxTR96t #3DGS #ComputerVision #3D