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pro vyhledávání: '"Kaplanyan, Anton"'
Autor:
Zhang, Yunxiang, Kuznetsov, Alexandr, Jindal, Akshay, Chen, Kenneth, Sochenov, Anton, Kaplanyan, Anton, Sun, Qi
Neural image representations have recently emerged as a promising technique for storing, streaming, and rendering visual data. Coupled with learning-based workflows, these novel representations have demonstrated remarkable visual fidelity and memory
Externí odkaz:
http://arxiv.org/abs/2407.01866
Despite significant advances in algorithms and hardware, global illumination continues to be a challenge in the real-time domain. Time constraints often force developers to either compromise on the quality of global illumination or disregard it altog
Externí odkaz:
http://arxiv.org/abs/2406.07906
In the wake of many new ML-inspired approaches for reconstructing and representing high-quality 3D content, recent hybrid and explicitly learned representations exhibit promising performance and quality characteristics. However, their scaling to high
Externí odkaz:
http://arxiv.org/abs/2405.20067
Autor:
Wu, Songyin, Vembar, Deepak, Sochenov, Anton, Panneer, Selvakumar, Kim, Sungye, Kaplanyan, Anton, Yan, Ling-Qi
Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution and high frame rate remains challenging. Frame extrapolation methods, which don't introduce additional latency as o
Externí odkaz:
http://arxiv.org/abs/2406.18551
Physically based rendering of complex scenes can be prohibitively costly with a potentially unbounded and uneven distribution of complexity across the rendered image. The goal of an ideal level of detail (LoD) method is to make rendering costs indepe
Externí odkaz:
http://arxiv.org/abs/2211.05932
Autor:
Neff, Thomas, Stadlbauer, Pascal, Parger, Mathias, Kurz, Andreas, Mueller, Joerg H., Chaitanya, Chakravarty R. Alla, Kaplanyan, Anton, Steinberger, Markus
Publikováno v:
Computer Graphics Forum Volume 40, Issue 4, 2021
The recent research explosion around implicit neural representations, such as NeRF, shows that there is immense potential for implicitly storing high-quality scene and lighting information in compact neural networks. However, one major limitation pre
Externí odkaz:
http://arxiv.org/abs/2103.03231
Modern computer vision algorithms have brought significant advancement to 3D geometry reconstruction. However, illumination and material reconstruction remain less studied, with current approaches assuming very simplified models for materials and ill
Externí odkaz:
http://arxiv.org/abs/1903.07145
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