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pro vyhledávání: '"Chou, Gene"'
Autor:
Tung, Joseph, Chou, Gene, Cai, Ruojin, Yang, Guandao, Zhang, Kai, Wetzstein, Gordon, Hariharan, Bharath, Snavely, Noah
Scene-level novel view synthesis (NVS) is fundamental to many vision and graphics applications. Recently, pose-conditioned diffusion models have led to significant progress by extracting 3D information from 2D foundation models, but these methods are
Externí odkaz:
http://arxiv.org/abs/2406.11819
Multi-task learning (MTL) aims to learn multiple tasks using a single model and jointly improve all of them assuming generalization and shared semantics. Reducing conflicts between tasks during joint learning is difficult and generally requires caref
Externí odkaz:
http://arxiv.org/abs/2309.16921
Autor:
Chakravarthula, Praneeth, Sun, Jipeng, Li, Xiao, Lei, Chenyang, Chou, Gene, Bijelic, Mario, Froesch, Johannes, Majumdar, Arka, Heide, Felix
Today's commodity camera systems rely on compound optics to map light originating from the scene to positions on the sensor where it gets recorded as an image. To record images without optical aberrations, i.e., deviations from Gauss' linear model of
Externí odkaz:
http://arxiv.org/abs/2308.02797
Probabilistic diffusion models have achieved state-of-the-art results for image synthesis, inpainting, and text-to-image tasks. However, they are still in the early stages of generating complex 3D shapes. This work proposes Diffusion-SDF, a generativ
Externí odkaz:
http://arxiv.org/abs/2211.13757
We investigate the generalization capabilities of neural signed distance functions (SDFs) for learning 3D object representations for unseen and unlabeled point clouds. Existing methods can fit SDFs to a handful of object classes and boast fine detail
Externí odkaz:
http://arxiv.org/abs/2206.02780
Conference
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