274 citations · 710 across the 42 of their papers we have counts for
8 papers · 1 filter
Light Stage Super-Resolution: Continuous High-Frequency Relighting
Tiancheng Sun, Zexiang Xu, Xiuming Zhang +6
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces. By capturing the appearance of the human subj…
Photon-Driven Neural Path Guiding
Shilin Zhu, Zexiang Xu, Tiancheng Sun +5
Although Monte Carlo path tracing is a simple and effective algorithm to synthesize photo-realistic images, it is often very slow to converge to noise-free results when involving c…
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 Multi Depth Panoramas for View Synthesis
Kai-En Lin, Zexiang Xu, Ben Mildenhall +6
We propose a learning-based approach for novel view synthesis for multi-camera 360 panorama capture rigs. Previous work constructs RGBD panoramas from such data, allowing…
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 Photon Mapping
Shilin Zhu, Zexiang Xu, Henrik Wann Jensen +2
Recently, deep learning-based denoising approaches have led to dramatic improvements in low sample-count Monte Carlo rendering. These approaches are aimed at path tracing, which is…
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…