Paperpile

Referenced Papers (10)

Weier Riviere Guseinov Garbin Slusallek Bickel Beeler Vicini Practical Inverse Rendering of Textured and Translucent Appearance

"This paper introduces a novel inverse rendering technique capable of recreating detailed 3D scenes and materials from diverse camera views and lighting conditions, even creating digital avatars and video game versions of scenes, without requiring neural networks."

Referenced at: 00:01

Every filter extracts A specific texture in convolutional neural networks

Zhiqiang Xia, Ce Zhu, Zhengtao Wang, Qi Guo, Yipeng Liu

arXiv [cs.CV], 2016

"This paper is cited as an example of a light simulation program that can reconstruct scenes from different camera views and lighting conditions, focusing on material recreation."

Referenced at: 00:12

NeRF: Representing scenes as neural radiance fields for view synthesis

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng

arXiv [cs.CV], 2020

"This work is presented as an example of previous techniques capable of rendering camera paths and view-dependent appearances from images."

Referenced at: 00:23

Interactive albedo editing in path-traced volumetric materials

Milovš Hašan, Ravi Ramamoorthi

ACM Trans. Graph., 2013

"This paper is cited for its contribution to subsurface scattering techniques, enabling the rendering of translucent objects and materials like skin, milk, and marble in real-time."

Referenced at: 01:02

Photon beam diffusion: A hybrid Monte Carlo method for subsurface scattering

Ralf Habel, Per H Christensen, Wojciech Jarosz

Comput. Graph. Forum, 2013

"Cited as inspiration for an image illustrating light scattering within an object, a concept foundational to translucent rendering."

Referenced at: 01:03

Computational mirrors: Blind inverse light transport by deep matrix factorization

Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B Yedidia, Gregory W Wornell, William T Freeman

arXiv [cs.CV], 2019

"This paper is cited for its ability to reconstruct materials by analyzing footage and recreating them in high quality, including translucency."

Referenced at: 01:46

Rethinking the training and evaluation of rich-context layout-to-image generation

Jiaxin Cheng, Zixu Zhao, Tong He, Tianjun Xiao, Yicong Zhou, Zheng Zhang

arXiv [cs.CV], 2024

"This is presented as a previous inverse rendering technique that can reconstruct scenes but often produces noisy or splotchy results."

Referenced at: 02:37

NeuMIP: Multi-Resolution Neural Materials

Alexandr Kuznetsov, Krishna Mullia, Zexiang Xu, Miloš Hašan, Ravi Ramamoorthi

arXiv [cs.GR], 2021

"Cited as a demonstration of a technique capable of interactively updating material properties like texture, glossiness, and subsurface scattering on complex objects."

Referenced at: 04:24

Photorealistic material editing through direct image manipulation

Károly Zsolnai-Fehér, Peter Wonka, Michael Wimmer

arXiv [cs.GR], 2019

"This paper explains the difference between simplified diffusion models and more accurate path tracing for rendering, illustrating that previous methods were shortcuts not reflecting reality."

Referenced at: 04:47

Specular manifold sampling for rendering high-frequency caustics and glints

Tizian Zeltner, Iliyan Georgiev, Wenzel Jakob

ACM Trans. Graph., 2020

"Cited to illustrate a truly unbiased path tracing technique, which is compared to a biased version for demonstrating the importance of accurate light simulation."

Referenced at: 05:33