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20152020
most citedSiamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation

43 citations · 75 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2020

Resolution Correspondence Networks

Georgi Tinchev, Shuda Li, Kai Han +2

In this paper, we aim at establishing accurate dense correspondences between a pair of images with overlapping field of view under challenging illumination variation, viewpoint cha…

cs.CV2018★ 1 cited

A Summary of the 4th International Workshop on Recovering 6D Object Pose

Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim +12

This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited…

cs.CV2017★ 3 cited

A Learning-based Variable Size Part Extraction Architecture for 6D Object Pose Recovery in Depth

Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim

State-of-the-art techniques for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To deal with this shortcoming, we introduc…

cs.CV2016★ 43 cited

Siamese Regression Networks with Efficient mid-level Feature Extraction for 3D Object Pose Estimation

Andreas Doumanoglou, Vassileios Balntas, Rigas Kouskouridas +1

In this paper we tackle the problem of estimating the 3D pose of object instances, using convolutional neural networks. State of the art methods usually solve the challenging probl…

cs.CV2016★ 10 cited

Iterative Hough Forest with Histogram of Control Points for 6 DoF Object Registration from Depth Images

Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim

State-of-the-art techniques proposed for 6D object pose recovery depend on occlusion-free point clouds to accurately register objects in 3D space. To reduce this dependency, we int…

cs.CV2016★ 6 cited

Latent-Class Hough Forests for 6 DoF Object Pose Estimation

Rigas Kouskouridas, Alykhan Tejani, Andreas Doumanoglou +2

In this paper we present Latent-Class Hough Forests, a method for object detection and 6 DoF pose estimation in heavily cluttered and occluded scenarios. We adapt a state of the ar…

cs.CV2015★ 12 cited

Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd

Andreas Doumanoglou, Rigas Kouskouridas, Sotiris Malassiotis +1

Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important probl…