Zobrazeno 1 - 10
of 306
pro vyhledávání: '"Chen, Wenzheng"'
Diffusion models have demonstrated exceptional ability in modeling complex image distributions, making them versatile plug-and-play priors for solving imaging inverse problems. However, their reliance on large-scale clean datasets for training limits
Externí odkaz:
http://arxiv.org/abs/2410.11241
Diffusion models (DMs) have emerged as powerful generative models for solving inverse problems, offering a good approximation of prior distributions of real-world image data. Typically, diffusion models rely on large-scale clean signals to accurately
Externí odkaz:
http://arxiv.org/abs/2407.11162
Diffusion models have emerged as powerful tools for solving inverse problems due to their exceptional ability to model complex prior distributions. However, existing methods predominantly assume known forward operators (i.e., non-blind), limiting the
Externí odkaz:
http://arxiv.org/abs/2407.01027
Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, clean datasets for training limits their practical use where clean data is scarce. In this paper, we prop
Externí odkaz:
http://arxiv.org/abs/2407.01014
We consider the problem of novel-view synthesis (NVS) for dynamic scenes. Recent neural approaches have accomplished exceptional NVS results for static 3D scenes, but extensions to 4D time-varying scenes remain non-trivial. Prior efforts often encode
Externí odkaz:
http://arxiv.org/abs/2402.03307
Autor:
Klinghoffer, Tzofi, Philion, Jonah, Chen, Wenzheng, Litany, Or, Gojcic, Zan, Joo, Jungseock, Raskar, Ramesh, Fidler, Sanja, Alvarez, Jose M.
Autonomous vehicles (AV) require that neural networks used for perception be robust to different viewpoints if they are to be deployed across many types of vehicles without the repeated cost of data collection and labeling for each. AV companies typi
Externí odkaz:
http://arxiv.org/abs/2309.05192
Autor:
Shen, Tianchang, Munkberg, Jacob, Hasselgren, Jon, Yin, Kangxue, Wang, Zian, Chen, Wenzheng, Gojcic, Zan, Fidler, Sanja, Sharp, Nicholas, Gao, Jun
Publikováno v:
ACM Transactions on Graphics, Volume 42, Issue 4, Article No.: 37, August 2023
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative mod
Externí odkaz:
http://arxiv.org/abs/2308.05371
Autor:
Huang, Binbin, Peng, Xingyue, Shen, Siyuan, Xia, Suan, Li, Ruiqian, Yu, Yanhua, Wang, Yuehan, Gao, Shenghua, Chen, Wenzheng, Li, Shiying, Yu, Jingyi
We introduce Omni-LOS, a neural computational imaging method for conducting holistic shape reconstruction (HSR) of complex objects utilizing a Single-Photon Avalanche Diode (SPAD)-based time-of-flight sensor. As illustrated in Fig. 1, our method enab
Externí odkaz:
http://arxiv.org/abs/2304.10780
Autor:
Wang, Zian, Shen, Tianchang, Gao, Jun, Huang, Shengyu, Munkberg, Jacob, Hasselgren, Jon, Gojcic, Zan, Chen, Wenzheng, Fidler, Sanja
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods achieve impressive fidelity of 3D reconstruction, but bake the lighti
Externí odkaz:
http://arxiv.org/abs/2304.03266
Autor:
Zhou, Taotao, He, Kai, Wu, Di, Xu, Teng, Zhang, Qixuan, Shao, Kuixiang, Chen, Wenzheng, Xu, Lan, Yu, Jingyi
Human modeling and relighting are two fundamental problems in computer vision and graphics, where high-quality datasets can largely facilitate related research. However, most existing human datasets only provide multi-view human images captured under
Externí odkaz:
http://arxiv.org/abs/2212.07648