BREAKING NEWS
LATEST POSTS
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Andrii Shramko – How to process 20,000 photos for a 3DGS model on a single RTX 4090 using GreenValley International Lidar360MLS
The goal was ambitious: to generate a hyper-detailed 3DGS scan from a massive dataset—20,000 drone photos at full resolution (5280x3956px). All of this on a single machine with just one RTX 4090 GPU.
What was the problem?
Most existing tools simply can’t handle this volume of data. For instance, Postshot, which is excellent for many tasks, confidently processed up to 7,000 photos but choked on 20,000—it ran for two days without even starting the model training.
The Breakthrough Solution.
The real discovery was the software from GreenValley Internationalhttps://www.greenvalleyintl.com/LiDAR360MLS
Their approach is brilliant: instead of trying to swallow the entire dataset at once, the program intelligently divides it into smaller, manageable chunks, trains each one individually, and then seamlessly merges them into one giant, detailed scene. After 40 hours of rendering, we got this stunning 103 million splats PLY result:
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AI and the Law – Netflix : Using Generative AI in Content Production
https://www.cartoonbrew.com/business/netflix-generative-ai-use-guidelines-253300.html
- Temporary Use: AI-generated material can be used for ideation, visualization, and exploration—but is currently considered temporary and not part of final deliverables.
- Ownership & Rights: All outputs must be carefully reviewed to ensure rights, copyright, and usage are properly cleared before integrating into production.
- Transparency: Productions are expected to document and disclose how generative AI is used.
- Human Oversight: AI tools are meant to support creative teams, not replace them—final decision-making rests with human creators.
- Security & Compliance: Any use of AI tools must align with Netflix’s security protocols and protect confidential production material.
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SkyworkAI Matrix-3D – Omnidirectional Explorable 3D World Generation
https://github.com/SkyworkAI/Matrix-3D
Matrix-3D utilizes panoramic representation for wide-coverage omnidirectional explorable 3D world generation that combines conditional video generation and panoramic 3D reconstruction.
- Large-Scale Scene Generation : Compared to existing scene generation approaches, Matrix-3D supports the generation of broader, more expansive scenes that allow for complete 360-degree free exploration.
- High Controllability : Matrix-3D supports both text and image inputs, with customizable trajectories and infinite extensibility.
- Strong Generalization Capability : Built upon self-developed 3D data and video model priors, Matrix-3D enables the generation of diverse and high-quality 3D scenes.
- Speed-Quality Balance: Two types of panoramic 3D reconstruction methods are proposed to achieve rapid and detailed 3D reconstruction respectively.
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Zibra.AI – Real-Time Volumetric Effects in Virtual Production. Now free for Indies!
A New Era for Volumetrics
For a long time, volumetric visual effects were viable only in high-end offline VFX workflows. Large data footprints and poor real-time rendering performance limited their use: most teams simply avoided volumetrics altogether. It’s similar to the early days of online video: limited computational power and low network bandwidth made video content hard to share or stream. Today, of course, we can’t imagine the internet without it, and we believe volumetrics are on a similar path.
With advanced data compression and real-time, GPU-driven decompression, anyone can now bring CGI-class visual effects into Unreal Engine.
From now on, it’s completely free for individual creators!
What it means for you?
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FEATURED POSTS
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Ashton Kutcher Says Soon ‘You’ll Be Able to Render a Whole Movie’ Using AI
https://variety.com/2024/film/news/ashton-kutcher-ai-movies-sora-hollywood-1236027196/
“I have a beta version of it and it’s pretty amazing,” Kutcher said of the platform in a recent conversation with former Google CEO Eric Schmidt at the Berggruen Salon in Los Angeles.
“Why would you go out and shoot an establishing shot of a house in a television show when you could just create the establishing shot for $100? To go out and shoot it would cost you thousands of dollars,” Kutcher said. “Action scenes of me jumping off of this building, you don’t have to have a stunt person go do it, you could just go do it [with AI].
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Space bodies’ components and light spectroscopy
www.plutorules.com/page-111-space-rocks.html
This help’s us understand the composition of components in/on solar system bodies.
Dips in the observed light spectrum, also known as, lines of absorption occur as gasses absorb energy from light at specific points along the light spectrum.
These dips or darkened zones (lines of absorption) leave a finger print which identify elements and compounds.
In this image the dark absorption bands appear as lines of emission which occur as the result of emitted not reflected (absorbed) light.
Lines of absorption
Lines of emission
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DiffusionLight: HDRI Light Probes for Free by Painting a Chrome Ball
https://diffusionlight.github.io/
https://github.com/DiffusionLight/DiffusionLight
https://github.com/DiffusionLight/DiffusionLight?tab=MIT-1-ov-file#readme
https://colab.research.google.com/drive/15pC4qb9mEtRYsW3utXkk-jnaeVxUy-0S
“a simple yet effective technique to estimate lighting in a single input image. Current techniques rely heavily on HDR panorama datasets to train neural networks to regress an input with limited field-of-view to a full environment map. However, these approaches often struggle with real-world, uncontrolled settings due to the limited diversity and size of their datasets. To address this problem, we leverage diffusion models trained on billions of standard images to render a chrome ball into the input image. Despite its simplicity, this task remains challenging: the diffusion models often insert incorrect or inconsistent objects and cannot readily generate images in HDR format. Our research uncovers a surprising relationship between the appearance of chrome balls and the initial diffusion noise map, which we utilize to consistently generate high-quality chrome balls. We further fine-tune an LDR difusion model (Stable Diffusion XL) with LoRA, enabling it to perform exposure bracketing for HDR light estimation. Our method produces convincing light estimates across diverse settings and demonstrates superior generalization to in-the-wild scenarios.”
