Ranch Computing’s Post

🌟 Transform Your Creative Process: Ultra-Fast Rendering! ⏱️ 🐌 Traditional Rendering:7 hours 🚀 Rendering on Ranch Computing: Just 14 minutes! Imagine what you could achieve if each render took only minutes instead of days. That's the power of Ranch Computing's advanced rendering technology. Save precious time and focus on what matters: your creativity. ⚡ Unleash your creative potential with faster renders. It's time for a revolution in the world of 3D rendering! #FastRendering #3DInnovation #LimitlessCreativity #TimeSaving #EfficiencyBoost

🌟 Transform Your Creative Process: Ultra-Fast Rendering! ⏱️ 🐌 Traditional Rendering: 43 min and 58 sec 🚀 Rendering on Ranch Computing: 5 min & 29 sec Imagine what you could achieve if each render took only minutes instead of days. That's the power of Ranch Computing's advanced rendering technology. Save precious time and focus on what matters: your creativity. ⚡ Unleash your creative potential with faster renders. It's time for a revolution in the world of 3D rendering! #FastRendering #3DInnovation #LimitlessCreativity #TimeSaving #EfficiencyBoost

🌟 Transform Your Creative Process: Ultra-Fast Rendering! ⏱️ 🐌 Traditional Rendering: 3 days and 10 hours 🚀 Rendering on Ranch Computing: Just 1 hour & 18 min Imagine what you could achieve if each render took only minutes instead of days. That's the power of Ranch Computing's advanced rendering technology. Save precious time and focus on what matters: your creativity. ⚡ Unleash your creative potential with faster renders. It's time for a revolution in the world of 3D rendering! #FastRendering #3DInnovation #LimitlessCreativity #TimeSaving #EfficiencyBoost

In continuation of our series on Eos insights, I thought this visualization was pretty cool. It represents the comprehensive Wilmot Technical Structure from the ground up. This demonstrates how Eos intuitively organizes elements through Child/Parent relationships, providing a clear overview of our substantial technical operations. The scale and complexity, even at a high level, are evident. Particularly noteworthy in scenarios involving thousands of nodes, the use of color coding and animations becomes crucial for rapid identification within the most intricate layers of our infrastructure. As an example, the blue lines in this image are indicative of the connections to our Web Applications, showcasing both the functionality and the intuitive design of the system. The cool thing about Eos is it's custom coded. The sky is the limit as they say. Omara Alvelo, Kevin Desousa, Scott Paoni #wcii #eos https://lnkd.in/efPPpFsY

More features have been added into nerfstudio, including Splatfacto-big. It's a more robust version of Splatfacto, consuming 12GB of VRAM. Other features introduce time as a variable for dynamic scene visualization.

A new post on my blog is out! The post is an overview of the virtual geometry system in my engine. It explains how such a system works and all the building blocks needed to implement it. Providing a link: https://lnkd.in/gbGUF76H

#OneRoomChallenge UPDATE! Logan's feedback sent us back to square one with our futuristic Govee Glide Y lights. 🚀 Using 3D photorealistic software, we're tweaking the design to get it just right. Excited to share the new look soon! 👉 https://lttr.ai/ASNZI #OneRoomChallenge #BlogPost #Beddys #SensorySensitivities

Collaborative Control for Geometry-Conditioned PBR Image Generation They propose a novel approach to 3D content generation by directly modeling the physically-based rendering (PBR) image distribution, linking a frozen RGB model with a newly trained PBR model through an innovative cross-network communication paradigm, addressing the challenges of photometric inaccuracies and the ambiguity of deriving PBR from RGB, while avoiding catastrophic forgetting and ensuring compatibility with existing RGB-based techniques. github and demo(The code is not yet available.): https://lnkd.in/grepnCYQ #stablediffusion #generatieveai #machinelearningmodels #machinelearningalgorithms

Something fundamental about ray traced / sampled rendering that I didn't appreciate for much too long: Small bright lights are a problem because they have a low chance of being hit, but they make a large impact in the result. This is literally high variance by definition and is essentially the entire source of rendering noise spatially. Real time rendering has temporal noise sources as well, but you can extend this to the time axis for the same thoughts - Quick, bright flashes of light cause high variance temporally! Something else worth mentioning about rendering noise is how sampling sequences and noise textures fit in. 1) Low discrepancy sampling like Sobol etc. make the error go away the fastest but look bad before the error is gone. 2) Spatiotemporal blue noise textures can give you samples (ray directions) to use that hide the error perceptually instead. 3) If you know how you are filtering your render, you can use FAST noise textures to make the noise pair to the filter better. FAST noise: https://lnkd.in/gsv37Nab Note: FAST noise can make spatiotemporal blue noise by optimizing for a gaussian filter spatially, and a gaussian filter temporally, separately. FAST noise is a superset of spatiotemporal blue noise, and makes higher quality STBN than the original STBN paper. The FAST noise repo has some pregenerated noise textures of various kinds including STBN, that you can pull down and use as a random number source in any per pixel stochastic rendering algorithm, to get better results than white noise, or low discrepancy sequences at low sample counts. See first comment for bonus content :)

Performing CPU brush drawing on Texture2D poses performance issues. The common solution is to use a GPU brush. However, there is currently limited adoption of the practice of drawing on Texture2DArray. The video demonstrates this aspect. https://lnkd.in/gMq6Wb4V