716 citations · 972 across the 6 of their papers we have counts for
6 papers
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…
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…
AdaBins: Depth Estimation using Adaptive Bins
Shariq Farooq Bhat, Ibraheem Alhashim, Peter Wonka
We address the problem of estimating a high quality dense depth map from a single RGB input image. We start out with a baseline encoder-decoder convolutional neural network archite…
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…
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…
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…