73 citations · 156 across the 3 of their papers we have counts for
5 papers · 1 filter
Bridging the Reality Gap for Pose Estimation Networks using Sensor-Based Domain Randomization
Frederik Hagelskjaer, Anders Glent Buch
Since the introduction of modern deep learning methods for object pose estimation, test accuracy and efficiency has increased significantly. For training, however, large amounts of…
PointVoteNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds
Frederik Hagelskjær, Anders Glent Buch
We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting…
BOP: Benchmark for 6D Object Pose Estimation
Tomas Hodan, Frank Michel, Eric Brachmann +13
We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the obj…
In search of inliers: 3d correspondence by local and global voting
Anders Glent Buch, Yang Yang, Norbert Krüger +1
We present a method for finding correspondence between 3D models. From an initial set of feature correspondences, our method uses a fast voting scheme to separate the inliers from…
Pose Estimation using Local Structure-Specific Shape and Appearance Context
Anders Glent Buch, Dirk Kraft, Joni-Kristian Kamarainen +2
We address the problem of estimating the alignment pose between two models using structure-specific local descriptors. Our descriptors are generated using a combination of 2D image…