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20162020
most citedNeural Reflectance Fields for Appearance Acquisition

110 citations · 142 across the 4 of their papers we have counts for

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7 papers · 1 filter

cs.CV2020110 cited

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…

cs.CV20206 cited

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…

cs.CV2020

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…

cs.CV20191 cited

Enforcing Reasoning in Visual Commonsense Reasoning

Hammad A. Ayyubi, Md. Mehrab Tanjim, David J. Kriegman

The task of Visual Commonsense Reasoning is extremely challenging in the sense that the model has to not only be able to answer a question given an image, but also be able to learn…

cs.CV2019

Detecting the Starting Frame of Actions in Video

Iljung S. Kwak, Jian-Zhong Guo, Adam Hantman +2

In this work, we address the problem of precisely localizing key frames of an action, for example, the precise time that a pitcher releases a baseball, or the precise time that a c…

cs.CV201725 cited

Image to Image Translation for Domain Adaptation

Zak Murez, Soheil Kolouri, David Kriegman +2

We propose a general framework for unsupervised domain adaptation, which allows deep neural networks trained on a source domain to be tested on a different target domain without re…