VirusTotal’s Post

Conducted Loftr(Local feature Based Transform) Analysis on Phantom Tomato Cluster as an attempt to reconstruct a 180 Degree Point-Cloud. With Each Iteration, the Model learns to Match the Feature Points of the Tomato Cluster by taking different angles of the Tomatoes thus increasing the Number of Matching Points. This Experiment Was conducted in RMML Lab of NTU ** These images were taken By Intel Realsense Camera D435 Depth Camera

Difference map between the results obtained enlarging a simple grayscale image 10x (in both directions) with two close variants of a prototype method. Difference maps tell me where to look for differences. (Black means not same in sRGB 8 bit. Original white on gray results---like the original---being compared faintly shown.)

In last week’s Thunder Session, Luca Antiga and Thomas Viehmann walk through activation checkpointing and how it’s used to cope with limited GPU memory. Watch the full episode: https://bit.ly/4fZ6r0V

 Day 5 of my #GfG160 Challenge – and today’s problem is a real gem: Next Permutation! Imagine you have a sequence of numbers, and your mission is to find its next lexicographical arrangement. Sounds simple? It’s a beautiful mix of logic and creativity! 🧠🎯 This problem pushes you to: 👉 Identify the turning point where the order changes. 👉 Swap, reverse, and voilà – the next permutation emerges! Every line of code feels like solving a puzzle, and GeeksforGeeks breaks it down with clarity. 💻💡 Excited to crack this one and move closer to 160! 🚀 Let’s keep growing, one algorithm at a time. 🏆 #gfg160 #geekstreak2024.

I'm excited to share our latest blog post that explores a groundbreaking advancement in visual technology: CRT simulation implemented in a GPU shader. This innovative approach offers a superior alternative to traditional Black Frame Insertion techniques, enhancing the viewing experience for gamers and enthusiasts alike. Dive into the details of how this simulation can significantly improve motion clarity and overall image quality. Discover the full insights here: https://ift.tt/xr5snmP. Your feedback and thoughts are greatly valued!

Excited to share an insightful blog post on real-time collision detection! This article introduces an easy distance field-based collision detection scheme, leveraging back-face culling and z-buffering. The solution is precise and efficient, relying on the GPU for performance. It's an easy-to-implement method that natively handles collisions with primitives and offers potential adaptations for certain particle systems. Read the full post here: https://bit.ly/4aSmeeK.

Released last month and looks very promising. A must try if you are still using u2net for background removal. Paper shows better numbers than u2net in the comparison done using the DIS5k dataset. 1. Works on higher quality images (1024x1024) 2. Only uses 5.5gb gpu memory for the above resolution ! Link to the paper: https://lnkd.in/gb_dfwk2 #computervision #backgroundremoval #ai #datascience

In our lab we have an NVIDIA RTX 3090 (24GB). It's a trustworthy GPU but it quickly hits a wall when running inference on models larger than ~6B params 🏋️. I heard a lot about quantization but did not know that you could quantize most model "on the spot". I totally recommend this short course to anyone hitting the limits of their GPU VRAM, and who hasn't tried  quantization yet 💪. https://lnkd.in/e82g6sVP

I worked on a project that involved creating a ball-dragging simulation, where I used grid spatial partitioning to significantly optimize the efficiency of the collision system. Instead of checking for collisions by iterating over all entities in the scene, I divided the space into cells and only considered the entities that were located in the cells adjacent to the cell of the entity being processed. This approach greatly reduced the CPU workload. The scene simulated approximately 30000 spheres, with minimal performance overhead, rendered using different shader effects.

Create a portable Mustache Camera with Computer Vision on a BeagleY-AI board to learn about #EdgeAI. Here is a snippet of a recent #IoTShow episode with Jason Kridner, the founder of BeagleBoard.org