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DTSTAMP:20250110T023312Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241205T105900
DTEND;TZID=Asia/Tokyo:20241205T111300
UID:siggraphasia_SIGGRAPH Asia 2024_sess128_papers_536@linklings.com
SUMMARY:3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes
DESCRIPTION:Technical Papers\n\nNicolas Moenne-Loccoz (NVIDIA); Ashkan Mir
 zaei (NVIDIA, University of Toronto); and Or Perel, Riccardo de Lutio, Jan
 ick Martinez Esturo, Gavriel State, Sanja Fidler, Nicholas Sharp, and Zan 
 Gojcic (NVIDIA)\n\nParticle-based representations of radiance fields such 
 as 3D Gaussian Splatting have found great success for reconstructing and r
 e-rendering of complex scenes.\nMost existing methods render particles via
  rasterization, projecting them to screen space tiles for processing in a 
 sorted order.\nThis work instead considers ray tracing the particles, buil
 ding a bounding volume hierarchy and casting a ray for each pixel using hi
 gh-performance GPU ray tracing hardware.\nTo efficiently handle large numb
 ers of semi-transparent particles, we describe a specialized rendering alg
 orithm which encapsulates particles with bounding meshes to leverage fast 
 ray-triangle intersections, and shades batches of intersections in depth-o
 rder. \nThe benefits of ray tracing are well-known in computer graphics: p
 rocessing incoherent rays for secondary lighting effects such as shadows a
 nd reflections, rendering from highly-distorted cameras common in robotics
 , stochastically sampling rays, and more.\nWith our renderer, this flexibi
 lity comes at little cost compared to rasterization. Experiments demonstra
 te the speed and accuracy of our approach, as well as several applications
  in computer graphics and vision.\nWe further propose related improvements
  to the basic Gaussian representation, including a simple use of generaliz
 ed kernel functions which significantly reduces particle hit counts.\n\nRe
 gistration Category: Full Access, Full Access Supporter\n\nLanguage Format
 : English Language\n\nSession Chair: Manolis Savva (Simon Fraser Universit
 y)
URL:https://asia.siggraph.org/2024/program/?id=papers_536&sess=sess128
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