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DTSTART:19721003T020000
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BEGIN:VEVENT
DTSTAMP:20260114T163714Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T140000
DTEND;TZID=Australia/Melbourne:20231213T150500
UID:siggraphasia_SIGGRAPH Asia 2023_sess128@linklings.com
SUMMARY:How To Deal With NERF?
DESCRIPTION:SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields w
 ith Simpler Solutions\n\nNeural Radiance Fields (NeRF) show impressive per
 formance for the photo-realistic free-view rendering of scenes. However, N
 eRFs require dense sampling of images in the given scene, and their perfor
 mance degrades significantly when only a sparse set of views are available
 . Researchers have found that...\n\n\nNagabhushan Somraj, Adithyan Karanay
 il, and Rajiv Soundararajan (Indian Institute of Science)\n---------------
 ------\nNeural Field Convolutions by Repeated Differentiation\n\nNeural fi
 elds are evolving towards a general-purpose continuous representation for 
 visual computing. Yet, despite their numerous appealing properties, they a
 re hardly amenable to signal processing. As a remedy, we present a method 
 to perform general continuous convolutions with general continuous si...\n
 \n\nNtumba Elie Nsampi, Adarsh Djeacoumar, and Hans-Peter Seidel (Max-Plan
 ck-Institut für Informatik); Tobias Ritschel (University College London (U
 CL)); and Thomas Leimkühler (Max-Planck-Institut für Informatik)\n--------
 -------------\nCamP: Camera Preconditioning for Neural Radiance Fields\n\n
 Neural Radiance Fields (NeRF) can be optimized to obtain high-fidelity 3D 
 scene reconstructions of objects and large-scale scenes. However, NeRFs re
 quire accurate camera parameters as input --- inaccurate camera parameters
  result in blurry renderings. Extrinsic and intrinsic camera parameters ar
 e us...\n\n\nKeunhong Park, Phillip Henzler, Ben Mildenhall, Jonathan T. B
 arron, and Ricardo Martin-Brualla (Google Research)\n---------------------
 \nGANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields\n\n
 Neural Radiance Fields (NeRF) have shown impressive novel view synthesis r
 esults; nonetheless, even thorough recordings yield imperfections in recon
 structions, for instance due to poorly observed areas or minor lighting ch
 anges.\nOur goal is to mitigate these imperfections from various sources w
 ith a...\n\n\nBarbara Roessle and Norman Müller (Technical University of M
 unich); Lorenzo Porzi, Samuel Rota Bulò, and Peter Kontschieder (Meta Real
 ity Labs); and Matthias Niessner (Technical University of Munich)\n-------
 --------------\nDreamEditor: Text-Driven 3D Scene Editing with Neural Fiel
 ds\n\nNeural fields have achieved impressive advancements in view synthesi
 s and scene reconstruction. However, editing these neural fields remains c
 hallenging due to the implicit encoding of geometry and texture informatio
 n. In this paper, we propose DreamEditor, a novel framework that enables u
 sers to pe...\n\n\nJingyu Zhuang (Sun Yat-sen University); Chen Wang (Univ
 ersity of Pennsylvania, Tsinghua University); Liang Lin (Sun Yat-sen Unive
 rsity); Lingjie Liu (University of Pennsylvania); and Guanbin Li (Sun Yat-
 sen University)\n\nRegistration Category: Full Access\n\nSession Chair: Ji
 anfei Cai (Monash University)
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