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This work instead considers ray tracing the particles, building a bounding volume hierarchy and casting a ray for each pixel using high-performance GPU ray tracing hardware. To efficiently handle large numbers of semi-transparent particles, we describe a specialized rendering algorithm which encapsulates particles with bounding meshes to leverage fast ray-triangle intersections, and shades batches of intersections in depth-order. The benefits of ray tracing are well-known in computer graphics: processing incoherent rays for secondary lighting effects such as shadows and reflections, rendering from highly-distorted cameras common in robotics, stochastically sampling rays, and more. With our renderer, this flexibility comes at little cost compared to rasterization. Experiments demonstrate the speed and accuracy of our approach, as well as several applications in computer graphics and vision. We further propose related improvements to the basic Gaussian representation, including a simple use of generalized kernel functions which significantly reduces particle hit counts.<\/jats:p>","DOI":"10.1145\/3687934","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T15:46:04Z","timestamp":1732031164000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":63,"title":["3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2312-9275","authenticated-orcid":false,"given":"Nicolas","family":"Moenne-Loccoz","sequence":"first","affiliation":[{"name":"NVIDIA, Montreal, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0890-1204","authenticated-orcid":false,"given":"Ashkan","family":"Mirzaei","sequence":"additional","affiliation":[{"name":"NVIDIA, Toronto, Canada"},{"name":"University of Toronto, Toronto, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6478-1422","authenticated-orcid":false,"given":"Or","family":"Perel","sequence":"additional","affiliation":[{"name":"NVIDIA, Tel Aviv, Israel"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2644-3876","authenticated-orcid":false,"given":"Riccardo","family":"de Lutio","sequence":"additional","affiliation":[{"name":"NVIDIA, San Francisco, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6907-5639","authenticated-orcid":false,"given":"Janick","family":"Martinez Esturo","sequence":"additional","affiliation":[{"name":"NVIDIA, Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3532-7352","authenticated-orcid":false,"given":"Gavriel","family":"State","sequence":"additional","affiliation":[{"name":"NVIDIA, Toronto, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1040-3260","authenticated-orcid":false,"given":"Sanja","family":"Fidler","sequence":"additional","affiliation":[{"name":"NVIDIA, Toronto, Canada"},{"name":"University of Toronto, Toronto, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2130-3735","authenticated-orcid":false,"given":"Nicholas","family":"Sharp","sequence":"additional","affiliation":[{"name":"NVIDIA, Seattle, United States of America"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6392-2158","authenticated-orcid":false,"given":"Zan","family":"Gojcic","sequence":"additional","affiliation":[{"name":"NVIDIA, Zurich, Switzerland"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"CoRR abs\/2201.00094","author":"Aizenshtein Maksim","year":"2022","unstructured":"Maksim Aizenshtein, Niklas Smal, and Morgan McGuire. 2022. 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