47 citations · 50 across the 5 of their papers we have counts for
6 papers
NeRF-Feat: 6D Object Pose Estimation using Feature Rendering
Shishir Reddy Vutukur, Heike Brock, Benjamin Busam +3
Object Pose Estimation is a crucial component in robotic grasping and augmented reality. Learning based approaches typically require training data from a highly accurate CAD model…
3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin
Sergey Zakharov, Wadim Kehl, Benjamin Planche +2
In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks. Insp…
Incremental Scene Synthesis
Benjamin Planche, Xuejian Rong, Ziyan Wu +5
We present a method to incrementally generate complete 2D or 3D scenes with the following properties: (a) it is globally consistent at each step according to a learned scene prior,…
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition
Benjamin Planche, Sergey Zakharov, Ziyan Wu +3
While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their train…
Keep it Unreal: Bridging the Realism Gap for 2.5D Recognition with Geometry Priors Only
Sergey Zakharov, Benjamin Planche, Ziyan Wu +3
With the increasing availability of large databases of 3D CAD models, depth-based recognition methods can be trained on an uncountable number of synthetically rendered images. Howe…
DepthSynth: Real-Time Realistic Synthetic Data Generation from CAD Models for 2.5D Recognition
Benjamin Planche, Ziyan Wu, Kai Ma +7
Recent progress in computer vision has been dominated by deep neural networks trained over large amounts of labeled data. Collecting such datasets is however a tedious, often impos…