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UID:siggraph_SIGGRAPH 2026_sess136@linklings.com
SUMMARY:3D Gaussian Splatting II
DESCRIPTION:Graphical X Splatting (GraphiXS): A Graphical Model for 4D Gau
 ssian Splatting under Uncertainty\n\nGraphical X Splatting (GraphiXS) is a
  novel probabilistic framework for systematically modelling data uncertain
 ty in 4D Gaussian Splatting. It handles various types of spatial and tempo
 ral sparsity, including missing frames and sparse viewpoints, and generali
 zes across diverse primitives. Extensive ...\n\n\nDoga Yilmaz (University 
 College London (UCL)); Jialin Zhu (Baidu Research); Deshan Gong (The Unive
 rsity of Hong Kong); and He Wang (University College London (UCL), Univers
 ity of Leeds)\n---------------------\nObject-Space Analysis of Local Contr
 ast Sensitivity for Hierarchical Representations of 3D Gaussians\n\nWe pre
 sent an object-space contrast sensitivity analysis for 3D Gaussian represe
 ntations that estimates spatial frequency responses without explicit raste
 rization using analytical Fourier transforms and the projection-slice theo
 rem. This enables contrast-sensitivity-based perceptual evaluation and e..
 .\n\n\nNaoto Yoshii and Suguru Saito (Institute of Science Tokyo) and Masa
 taka Sawayama and Yoshinori Dobashi (Hokkaido University, Prometech CG Res
 earch)\n---------------------\nLearning View-Dependent Splatting Kernels\n
 \nWe present a differentiable framework to automatically learn view-depend
 ent 2D kernels in a splatting-based pipeline to improve reconstruction qua
 lity and representation efficiency for novel 3D view synthesis.\n\n\nHuake
 ng Ding, Zhanpeng Liu, Fan Pei, Kun Zhou, and Hongzhi Wu (State Key Lab of
  CAD and CG, Zhejiang University)\n---------------------\nCAdam: Context-A
 daptive Moment Estimation for 3D Gaussian Densification in Generative Dist
 illation\n\nCAdam resolves the mismatch between reconstruction-native dens
 ification and stochastic generative guidance in optimization-based 3DGS. B
 y reinterpreting densification as statistical signal verification via mome
 ntum and context-adaptive selection, it reduces Gaussian primitives by 85%
 –97% acros...\n\n\nSeungJeh Chung (Kyung Hee University), Geonho Park (Kor
 ea University), Misong Kim (Kyung Hee University), and HyeongYeop Kang (Ko
 rea University)\n---------------------\nRadiance Fields from Photons\n\nNe
 ural radiance fields and Gaussian splats often produce artifacts in low li
 ght, high dynamic range, or with rapid motion. Instead, by training at the
  granularity of individual photons, as enabled by single-photon cameras, w
 e can achieve sharp, reliable reconstructions even in these challenging co
 nd...\n\n\nSacha Jungerman, Aryan Garg, and Mohit Gupta (University of Wis
 consin-Madison) and Sacha Jungerman\n---------------------\nRef-DGS: Refle
 ctive Dual Gaussian Splatting\n\nRef-DGS is a dual Gaussian splatting fram
 ework that decouples surface reconstruction and specular reflection, model
 ing near- and far-field specular reflections without ray tracing, achievin
 g efficient, high-quality surface reconstruction and novel view synthesis.
 \n\n\nNingjing Fan and Yiqun Wang (Chongqing University); Dong-Ming Yan (I
 nstitute of Automation, Chinese Academy of Sciences); and Peter Wonka (Kin
 g Abdullah University of Science and Technology (KAUST))\n----------------
 -----\n3D Gaussian Splatting II - Interactive Discussion\n\nAfter the summ
 ary presentations, attendees will participate in an interactive discussion
 . Outside the room will be a series of poster boards for authors to gather
  around with the audience. Authors are invited to bring any material relat
 ed to their paper that could instigate further conversation such...\n\n\nI
 nterest Area: Research & Education\n\nKeyword: Artificial Intelligence/Mac
 hine Learning, Geometry, Modeling, Rendering\n\nRegistration Category: Ful
 l Conference Supporter, Full Conference\n\nSession Chair: Rafał K. Mantiuk
  (University of Cambridge, Meta)
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