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Titlebook: Computer Vision – ECCV 2022; 17th European Confer Shai Avidan,Gabriel Brostow,Tal Hassner Conference proceedings 2022 The Editor(s) (if app

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51#
發(fā)表于 2025-3-30 08:35:09 | 只看該作者
52#
發(fā)表于 2025-3-30 14:46:24 | 只看該作者
53#
發(fā)表于 2025-3-30 20:34:51 | 只看該作者
,ARAH: Animatable Volume Rendering of?Articulated Human SDFs,s ray-surface intersection search and correspondence search. Our algorithm enables efficient point sampling and accurate point canonicalization while generalizing well to unseen poses. We demonstrate that our proposed pipeline can generate clothed avatars with high-quality pose-dependent geometry an
54#
發(fā)表于 2025-3-31 00:10:01 | 只看該作者
,ASpanFormer: Detector-Free Image Matching with?Adaptive Span Transformer,t also enable fine-grained attention among pixels of high relevance that compensates essential locality and piece-wise smoothness in matching tasks. State-of-the-art accuracy on a wide range of evaluation benchmarks validates the strong matching capability of our method.
55#
發(fā)表于 2025-3-31 03:17:38 | 只看該作者
56#
發(fā)表于 2025-3-31 06:54:06 | 只看該作者
57#
發(fā)表于 2025-3-31 09:57:58 | 只看該作者
,BungeeNeRF: Progressive Neural Radiance Field for?Extreme Multi-scale Scene Rendering,f-detail rendering across drastically varied scales. Starting from fitting distant views with a shallow base block, as training progresses, new blocks are appended to accommodate the emerging details in the increasingly closer views. The strategy progressively activates high-frequency channels in Ne
58#
發(fā)表于 2025-3-31 16:47:33 | 只看該作者
,Decomposing the?Tangent of?Occluding Boundaries According to?Curvatures and?Torsions,undary exhibits a strikingly elegant decomposition along the viewing direction and its orthogonal tangent, where the decomposition weights precisely match the geodesic torsion and the normal curvature of the surface respectively only along the line-of-sight! Though the focus of this paper is an enha
59#
發(fā)表于 2025-3-31 19:09:00 | 只看該作者
60#
發(fā)表于 2025-3-31 23:11:52 | 只看該作者
Generalizable Patch-Based Neural Rendering,coding, we parameterize rays as in a light field representation, with the crucial difference that the coordinates are canonicalized with respect to the target ray, which makes our method independent of the reference frame and improves generalization. We show that our approach outperforms the state-o
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