274 citations · 710 across the 42 of their papers we have counts for
9 papers · 1 filter
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
Titas Anciukevičius, Zexiang Xu, Matthew Fisher +4
Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support task…
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Yu-Ying Yeh, Zhengqin Li, Yannick Hold-Geoffroy +5
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout. In this work, we go beyond this to propose PhotoScene, a framework that takes input im…
RigNeRF: Fully Controllable Neural 3D Portraits
ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli +2
Volumetric neural rendering methods, such as neural radiance fields (NeRFs), have enabled photo-realistic novel view synthesis. However, in their standard form, NeRFs do not suppor…
ARF: Artistic Radiance Fields
Kai Zhang, Nick Kolkin, Sai Bi +4
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene. Previous methods that perform 3D stylization on point clouds or meshes are sen…
Differentiable Rendering of Neural SDFs through Reparameterization
Sai Praveen Bangaru, Michaël Gharbi, Tzu-Mao Li +7
We present a method to automatically compute correct gradients with respect to geometric scene parameters in neural SDF renderers. Recent physically-based differentiable rendering…
Physically-Based Editing of Indoor Scene Lighting from a Single Image
Zhengqin Li, Jia Shi, Sai Bi +6
We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that…
NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction
Xiaoshuai Zhang, Sai Bi, Kalyan Sunkavalli +2
While NeRF has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale…
TensoRF: Tensorial Radiance Fields
Anpei Chen, Zexiang Xu, Andreas Geiger +2
We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which repre…
Point-NeRF: Point-based Neural Radiance Fields
Qiangeng Xu, Zexiang Xu, Julien Philip +4
Volumetric neural rendering methods like NeRF generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand…