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🧵(3/4) 📜 Paper 3 - DGF: A Dense, Hardware-Friendly Geometry Format for Lossily Compressing Meshlets with Arbitrary Topologies. DGF will do for geometry what block compression did for textures, enabling #raytracing of massive micropolygon geometry 🗜️

14,201 просмотров • 2 лет назад •via X (Twitter)

Комментарии: 4

Фото профиля AMD GPUOpen
AMD GPUOpen2 лет назад

🧵(1/4) We'll be at @HPG_Conf, Denver, July 27! We're very excited to share the four papers we'll be presenting in this thread 🔥 📜 Paper 1 - HIPRT: Our #raytracing framework in HIP Take a look (28MB): Learn more about #HIP RT:

Фото профиля AMD GPUOpen
AMD GPUOpen2 лет назад

🧵(2/4) @HPG_Conf 📜 Paper 2 - H-PLOC: Super-fast, high-quality BVH construction. Allows for constructing a BVH over millions of triangles in just a few ms. Very useful for efficiently #raytracing a large amount of fully dynamic geometry per frame 🎇

Фото профиля AMD GPUOpen
AMD GPUOpen2 лет назад

🧵(4/4) @HPG_Conf 📜 Paper 4 - Real-Time Procedural Generation with GPU Work Graphs This paper shows some of the procedural effects from our #GDC2024 #WorkGraphs demo! 🏰 (32MB) Demo:

Фото профиля Brian Karis
Brian Karis2 лет назад

Very happy to see this published! DGF is very similar to Nanite's encoding minus attributes. Switching Nanite to this format would be some work but is definitely possible. Doing so wouldn't be much of a win for rasterization but the really exciting part here is ray tracing it.

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Randall Carlson

11,848 просмотров • 4 месяцев назад

Wow. Recreating the Shawshank Redemption prison in 3D from a single video, in real time (!) Just read the MASt3R-SLAM paper and it's pretty neat. These folks basically built a real-time dense SLAM system on top of MASt3R, which is a transformer-based neural network that can do 3d reconstruction and localization from uncalibrated image pairs. The cool part is they don't need a fixed camera model -- it just works with arbitrary cameras -- think different focal lengths, sensor sizes, even handling zooming in video (FMV drone video anyone?!). If you've done photogrammetry or played with NeRFs you know that is a HUGE deal. They've solved some tricky problems like efficient point matching and tracking, plus they've figured out how to fuse point clouds and handle loop closures in real-time. Their system runs at about 15 FPS on a 4090 and produces both camera poses and dense geometry. When they know the camera calibration, they get SOTA results across several benchmarks, but even without calibration, they still perform well. What's interesting is the approach -- most recent SLAM work has built on DROID-SLAM's architecture, but these folks went a different direction by leveraging a strong 3D reconstruction prior. Seems to give them more coherent geometry, which makes sense since that's what MASt3R was designed for. For anyone who cares about monocular SLAM and 3D reconstruction, this feels like a significant step toward plug-and-play dense SLAM without calibration headaches -- perfect for drones, robots, AR/VR -- the works!

Bilawal Sidhu

704,008 просмотров • 1 год назад