Zobrazeno 1 - 10
of 467
pro vyhledávání: '"P. P. Prokudin"'
3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significantly deteriorates when test views deviate from the camera angles used d
Externí odkaz:
http://arxiv.org/abs/2411.06390
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multilayer perceptrons (MLPs). However, MLPs are known to exhibit a low-fre
Externí odkaz:
http://arxiv.org/abs/2410.05050
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
Autor:
Zhang, Deheng, Wang, Jingyu, Wang, Shaofei, Mihajlovic, Marko, Prokudin, Sergey, Lensch, Hendrik P. A., Tang, Siyu
In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enabling high-quality relighting. The cornerstone of our method is a two-stag
Externí odkaz:
http://arxiv.org/abs/2409.20140
Autor:
Mihajlovic, Marko, Prokudin, Sergey, Tang, Siyu, Maier, Robert, Bogo, Federica, Tung, Tony, Boyer, Edmond
Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerged as a practical and scalable reconstruction method, gaining populari
Externí odkaz:
http://arxiv.org/abs/2409.11211
Understanding the dynamics of generic 3D scenes is fundamentally challenging in computer vision, essential in enhancing applications related to scene reconstruction, motion tracking, and avatar creation. In this work, we address the task as the probl
Externí odkaz:
http://arxiv.org/abs/2406.03625
Autor:
Hein, Jonas, Giraud, Frédéric, Calvet, Lilian, Schwarz, Alexander, Cavalcanti, Nicola Alessandro, Prokudin, Sergey, Farshad, Mazda, Tang, Siyu, Pollefeys, Marc, Carrillo, Fabio, Fürnstahl, Philipp
Surgery digitalization is the process of creating a virtual replica of real-world surgery, also referred to as a surgical digital twin (SDT). It has significant applications in various fields such as education and training, surgical planning, and aut
Externí odkaz:
http://arxiv.org/abs/2403.16736
Autor:
Boglione, Mariaelena, D'Alesio, Umberto, Flore, Carlo, Gonzalez-Hernandez, Josè Osvaldo, Murgia, Francesco, Prokudin, Alexei
The Bayesian reweighting procedure is extended to the case of multiple independent extractions of transverse momentum dependent parton distributions (TMDs). By exploiting the data on transverse single spin asymmetries, $A_N$, for inclusive pion produ
Externí odkaz:
http://arxiv.org/abs/2402.12322
We establish robust relations between Transverse Momentum Dependent distributions (TMDs) and collinear distributions. We define weighted integrals of TMDs that we call Transverse Momentum Moments (TMMs) and prove that TMMs are equal to collinear dist
Externí odkaz:
http://arxiv.org/abs/2402.01836
Recent advances in generative diffusion models have enabled the previously unfeasible capability of generating 3D assets from a single input image or a text prompt. In this work, we aim to enhance the quality and functionality of these models for the
Externí odkaz:
http://arxiv.org/abs/2401.04728
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fi
Externí odkaz:
http://arxiv.org/abs/2309.03160