activity
20152022
most citedTileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

61 citations · 67 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20224 cited

LocalBins: Improving Depth Estimation by Learning Local Distributions

Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka

We propose a novel architecture for depth estimation from a single image. The architecture itself is based on the popular encoder-decoder architecture that is frequently used as a…

cs.CV20211 cited

Self-Supervised Learning of Domain Invariant Features for Depth Estimation

Hiroyasu Akada, Shariq Farooq Bhat, Ibraheem Alhashim +1

We tackle the problem of unsupervised synthetic-to-real domain adaptation for single image depth estimation. An essential building block of single image depth estimation is an enco…

cs.GR201961 cited

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

Anna Frühstück, Ibraheem Alhashim, Peter Wonka

We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks…

cs.CV2018

High Quality Monocular Depth Estimation via Transfer Learning

Ibraheem Alhashim, Peter Wonka

Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction. Existing solutions for depth estimation often pro…

cs.GR20151 cited

Modeling and Correspondence of Topologically Complex 3D Shapes

Ibraheem Alhashim

3D shape creation and modeling remains a challenging task especially for novice users. Many methods in the field of computer graphics have been proposed to automate the often repet…