activity
20162022
most citedMarrNet: 3D Shape Reconstruction via 2.5D Sketches

237 citations · 497 across the 7 of their papers we have counts for

collaborators

17 papers

cs.CV20221 cited

AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training

Yifan Jiang, Peter Hedman, Ben Mildenhall +4

Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function. Though NeRF is able to render complex 3D scenes with view-dependent e…

cs.CV20211 cited

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

Shumian Xin, Neal Wadhwa, Tianfan Xue +5

We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map -- the amount of defocus blur at each pixel -- and recovers…

eess.IV2020

How to Train Neural Networks for Flare Removal

Yicheng Wu, Qiurui He, Tianfan Xue +4

When a camera is pointed at a strong light source, the resulting photograph may contain lens flare artifacts. Flares appear in a wide variety of patterns (halos, streaks, color ble…

cs.CV2020

Real-time Localized Photorealistic Video Style Transfer

Xide Xia, Tianfan Xue, Wei-sheng Lai +4

We present a novel algorithm for transferring artistic styles of semantically meaningful local regions of an image onto local regions of a target video while preserving its photore…

cs.CV2020

Learned Dual-View Reflection Removal

Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron +4

Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconv…

cs.CV2020

Neural Light Transport for Relighting and View Synthesis

Xiuming Zhang, Sean Fanello, Yun-Ta Tsai +10

The light transport (LT) of a scene describes how it appears under different lighting and viewing directions, and complete knowledge of a scene's LT enables the synthesis of novel…