283 citations · 608 across the 24 of their papers we have counts for
16 papers · 1 filter
Intrinsic decomposition and editing of 3D Gaussian splats
Alexandre Lanvin, Jeffrey Hu, Simon Lucas +2
Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image edit…
GRay: Ray Tracing 3D Gaussians Near the Speed of Splats
Yohan Poirier-Ginter, Jean-François Lalonde, George Drettakis
3D Gaussian Splatting (3DGS) is a popular representation for radiance field reconstruction, distinguished by the rendering speed of its rasterization-based renderer. While 3D Gauss…
Editable Physically-based Reflections in Raytraced Gaussian Radiance Fields
Yohan Poirier-Ginter, Jeffrey Hu, Jean-François Lalonde +1
Radiance fields such as 3D Gaussian Splatting allow real-time rendering of scenes captured from photos. They also reconstruct most specular reflections with high visual quality, bu…
Lighting-Consistent Object Transfer Across Radiance Fields
Nicolás Violante, George Kopanas, Linus Franke +2
3D Gaussian Splatting (3DGS) is widely used to capture and render real scenes. Compositing objects from one capture into another has applications in many domains, such as VFX, arch…
Splat and Replace: 3D Reconstruction with Repetitive Elements
Nicolás Violante, Andreas Meuleman, Alban Gauthier +3
We leverage repetitive elements in 3D scenes to improve novel view synthesis. Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly improved novel view synthe…
Does 3D Gaussian Splatting Need Accurate Volumetric Rendering?
Adam Celarek, George Kopanas, George Drettakis +2
Since its introduction, 3D Gaussian Splatting (3DGS) has become an important reference method for learning 3D representations of a captured scene, allowing real-time novel-view syn…
Learning Images Across Scales Using Adversarial Training
Krzysztof Wolski, Adarsh Djeacoumar, Alireza Javanmardi +7
The real world exhibits rich structure and detail across many scales of observation. It is difficult, however, to capture and represent a broad spectrum of scales using ordinary im…
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler +1
Radiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires ne…
Neural Point Catacaustics for Novel-View Synthesis of Reflections
Georgios Kopanas, Thomas Leimkühler, Gilles Rainer +2
View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they…
Deep scene-scale material estimation from multi-view indoor captures
Siddhant Prakash, Gilles Rainer, Adrien Bousseau +1
The movie and video game industries have adopted photogrammetry as a way to create digital 3D assets from multiple photographs of a real-world scene. But photogrammetry algorithms…
Active Exploration for Neural Global Illumination of Variable Scenes
Stavros Diolatzis, Julien Philip, George Drettakis
Neural rendering algorithms introduce a fundamentally new approach for photorealistic rendering, typically by learning a neural representation of illumination on large numbers of g…
FreeStyleGAN: Free-view Editable Portrait Rendering with the Camera Manifold
Thomas Leimkühler, George Drettakis
Current Generative Adversarial Networks (GANs) produce photorealistic renderings of portrait images. Embedding real images into the latent space of such models enables high-level i…
Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
Julien Philip, Sébastien Morgenthaler, Michaël Gharbi +1
We introduce a neural relighting algorithm for captured indoors scenes, that allows interactive free-viewpoint navigation. Our method allows illumination to be changed syntheticall…
Guided Fine-Tuning for Large-Scale Material Transfer
Valentin Deschaintre, George Drettakis, Adrien Bousseau
We present a method to transfer the appearance of one or a few exemplar SVBRDFs to a target image representing similar materials. Our solution is extremely simple: we fine-tune a d…
Flexible SVBRDF Capture with a Multi-Image Deep Network
Valentin Deschaintre, Miika Aittala, Fredo Durand +2
Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast…
Single-Image SVBRDF Capture with a Rendering-Aware Deep Network
Valentin Deschaintre, Miika Aittala, Fredo Durand +2
Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional…