15 citations · 50 across the 8 of their papers we have counts for
10 papers · 1 filter
Fast Light-Weight Near-Field Photometric Stereo
Daniel Lichy, Soumyadip Sengupta, David W. Jacobs
We introduce the first end-to-end learning-based solution to near-field Photometric Stereo (PS), where the light sources are close to the object of interest. This setup is especial…
Shape and Material Capture at Home
Daniel Lichy, Jiaye Wu, Soumyadip Sengupta +1
In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod. We propose a simple data capt…
SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
Koutilya PNVR, Hao Zhou, David Jacobs
We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping bot…
Neural Inverse Rendering of an Indoor Scene from a Single Image
Soumyadip Sengupta, Jinwei Gu, Kihwan Kim +3
Inverse rendering aims to estimate physical attributes of a scene, e.g., reflectance, geometry, and lighting, from image(s). Inverse rendering has been studied primarily for single…
Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images
Hao Zhou, Jin Sun, Yaser Yacoob +1
Lighting estimation from face images is an important task and has applications in many areas such as image editing, intrinsic image decomposition, and image forgery detection. We p…
Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing
Jin Sun, David W. Jacobs
Most of computer vision focuses on what is in an image. We propose to train a standalone object-centric context representation to perform the opposite task: seeing what is not ther…