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DTSTAMP:20260805T205521Z
LOCATION:Room 408 A
DTSTART;TZID=America/Los_Angeles:20260720T140000
DTEND;TZID=America/Los_Angeles:20260720T153000
UID:siggraph_SIGGRAPH 2026_sess137@linklings.com
SUMMARY:3D Gaussian Splatting I
DESCRIPTION:Hybrid Gaussian Wang Tiles for Class-aware Authoring and Rende
 ring\n\nWe present a multi-class Gaussian Splatting Wang Tiles framework f
 or synthesizing large, detailed scenes from multiple exemplars. Our method
  enables smooth transitions via hybrid tile mixing, supports real-time ren
 dering with hybrid CPU–GPU sorting, and provides an interactive authoring 
 tool f...\n\n\nYunfan Zeng (The Hong Kong University of Science and Techno
 logy), Li Ma (Eyeline), and Pedro V. Sander (The Hong Kong University of S
 cience and Technology)\n---------------------\nFaster 3D Gaussian Splattin
 g Convergence via Structure-Aware Densification\n\nWe present a structure-
 aware densification method for 3D Gaussian Splatting that adapts primitive
 s based on local texture frequency. By combining multi-scale analysis and 
 anisotropic splitting, it preserves fine details while avoiding unnecessar
 y density, enabling faster training and higher-quality ...\n\n\nLinjie Lyu
  (Max Planck Institute for Informatics); Ayush Tewari (Cambridge Universit
 y); and Jianchun Chen, Thomas Leimkuehler, and Christian Theobalt (Max Pla
 nck Institute for Informatics)\n---------------------\nGaussian Point Spla
 tting\n\nWe propose Gaussian point splatting, a stochastic method for rend
 ering massive 3DGS scenes. By sampling pixel-sized opaque points, splattin
 g them atomically, and applying stochastic transparency, we eliminate the 
 need for sorting. Our approach distributes workload evenly across GPU thre
 ads, enabling...\n\n\nJoris Rijsdijk, Christoph Peters, Michael Weinmann, 
 and Ricardo Marroquim (Delft University of Technology)\n------------------
 ---\nSHARP-GS: Scalable High-fidelity Accelerated Rendering Pipeline for U
 ltra-high Resolution 3D Gaussian Splatting\n\nThis work introduces a high-
 performance 3D Gaussian Splatting framework that unlocks real-time 8K rend
 ering for immersive VR. By overcoming architectural bottlenecks, it achiev
 es a massive 2.55× speedup and sustains over 250 FPS, delivering high-fide
 lity, 120 Hz stereoscopic experiences without comp...\n\n\nJunRan Ding, We
 iHang Liu, YuKe Li, YiZhou Wang, AnTong Li, QiHan Ding, Xin Lou, and JingY
 i Yu (ShanghaiTech University)\n---------------------\nA LoD of Gaussians:
  Out-of-Core Training and Rendering for Seamless Ultra-Large Scene Reconst
 ruction\n\nGaussian Splatting struggles with large scenes due to memory li
 mits and chunking artifacts. We introduce "A LoD of Gaussians," a new fram
 ework enabling ultra-large scene training and rendering on a single consum
 er GPU. Through out-of-core streaming and Level-of-Detail, it achieves sea
 mless multi-sca...\n\n\nFelix Windisch, Thomas Köhler, Lukas Radl, Mattia 
 D'urso, and Michael Steiner (Graz University of Technology); Dieter Schmal
 stieg (University of Stuttgart, Graz University of Technology); and Markus
  Steinberger (Graz University of Technology)\n---------------------\nMobil
 e3DGS³: Accelerate Mobile 3DGS Rendering via Gradient-Aware Super-Sampling
  and Frame Interpolation\n\nWe propose Mobile3DGS³, the first mobile 3DGS 
 acceleration paradigm solving high-res real-time rendering pain points via
  gradient-assisted post-rendering. It features TSGC, GASS, GAFI for altern
 ating super-sampling/interpolation, delivering high-fidelity efficiency.\n
 \n\nFan Gao, YIbo Zhao, Changhao Song, Jiarui Wen, Yuxuan Song, Youcheng C
 ai, and Ligang Liu (University of Science and Technology of China)\n------
 ---------------\n3D Gaussian Splatting I - Interactive Discussion\n\nAfter
  the summary presentations, attendees will participate in an interactive d
 iscussion. Outside the room will be a series of poster boards for authors 
 to gather around with the audience. Authors are invited to bring any mater
 ial related to their paper that could instigate further conversation such.
 ..\n\n\nInterest Area: Research & Education\n\nKeyword: Artificial Intelli
 gence/Machine Learning, Geometry, Modeling, Real-Time, Rendering\n\nRegist
 ration Category: Full Conference Supporter, Full Conference\n\nSession Cha
 ir: Young Min Kim (Seoul National University)
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