9 citations · 31 across the 8 of their papers we have counts for
5 papers · 1 filter
DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth
Jameel Malik, Ahmed Elhayek, Fabrizio Nunnari +4
Articulated hand pose and shape estimation is an important problem for vision-based applications such as augmented reality and animation. In contrast to the existing methods which…
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
Kripasindhu Sarkar, Basavaraj Hampiholi, Kiran Varanasi +1
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs. We represent 3D shapes as multi-layered height-maps (MLH) where at each grid locatio…
Fast Feature Extraction with CNNs with Pooling Layers
Christian Bailer, Tewodros Habtegebrial, Kiran varanasi +1
In recent years, many publications showed that convolutional neural network based features can have a superior performance to engineered features. However, not much effort was take…
Fast View Synthesis with Deep Stereo Vision
Tewodros Habtegebrial, Kiran Varanasi, Christian Bailer +1
Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the la…
HDM-Net: Monocular Non-Rigid 3D Reconstruction with Learned Deformation Model
Vladislav Golyanik, Soshi Shimada, Kiran Varanasi +1
Monocular dense 3D reconstruction of deformable objects is a hard ill-posed problem in computer vision. Current techniques either require dense correspondences and rely on motion a…