110 citations · 177 across the 18 of their papers we have counts for
19 papers
Neural Directional Encoding for Efficient and Accurate View-Dependent Appearance Modeling
Liwen Wu, Sai Bi, Zexiang Xu +5
Novel-view synthesis of specular objects like shiny metals or glossy paints remains a significant challenge. Not only the glossy appearance but also global illumination effects, in…
Controllable Dynamic Appearance for Neural 3D Portraits
ShahRukh Athar, Zhixin Shu, Zexiang Xu +4
Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and vie…
Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
Nithin Raghavan, Yan Xiao, Kai-En Lin +5
Precomputed Radiance Transfer (PRT) remains an attractive solution for real-time rendering of complex light transport effects such as glossy global illumination. After precomputati…
NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support
Xinyue Wei, Fanbo Xiang, Sai Bi +4
We present a method for generating high-quality watertight manifold meshes from multi-view input images. Existing volumetric rendering methods are robust in optimization but tend t…
TensoIR: Tensorial Inverse Rendering
Haian Jin, Isabella Liu, Peijia Xu +6
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus sufferin…
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields
Zhengfei Kuang, Fujun Luan, Sai Bi +3
Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis. However, it remains underexplored how the app…
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…
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…
Learning Neural Transmittance for Efficient Rendering of Reflectance Fields
Mohammad Shafiei, Sai Bi, Zhengqin Li +3
Recently neural volumetric representations such as neural reflectance fields have been widely applied to faithfully reproduce the appearance of real-world objects and scenes under…
NeLF: Neural Light-transport Field for Portrait View Synthesis and Relighting
Tiancheng Sun, Kai-En Lin, Sai Bi +2
Human portraits exhibit various appearances when observed from different views under different lighting conditions. We can easily imagine how the face will look like in another set…
Neural Reflectance Fields for Appearance Acquisition
Sai Bi, Zexiang Xu, Pratul Srinivasan +6
We present Neural Reflectance Fields, a novel deep scene representation that encodes volume density, normal and reflectance properties at any 3D point in a scene using a fully-conn…
Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images
Sai Bi, Zexiang Xu, Kalyan Sunkavalli +4
We present a deep learning approach to reconstruct scene appearance from unstructured images captured under collocated point lighting. At the heart of Deep Reflectance Volumes is a…
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Zhengqin Li, Ting-Wei Yu, Shen Sang +14
We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make th…
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement
Sai Bi, Kalyan Sunkavalli, Federico Perazzi +3
We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image spac…
Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images
Sai Bi, Zexiang Xu, Kalyan Sunkavalli +2
We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images…
Deep Hybrid Real and Synthetic Training for Intrinsic Decomposition
Sai Bi, Nima Khademi Kalantari, Ravi Ramamoorthi
Intrinsic image decomposition is the process of separating the reflectance and shading layers of an image, which is a challenging and underdetermined problem. In this paper, we pro…