110 citations · 122 across the 5 of their papers we have counts for
8 papers
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…
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…
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…