Search Results for author: Rigas Kouskouridas

Found 8 papers, 1 papers with code

$\mathbb{X}$Resolution Correspondence Networks

1 code implementation17 Dec 2020 Georgi Tinchev, Shuda Li, Kai Han, David Mitchell, Rigas Kouskouridas

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 changes, and style differences.

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

no code implementations9 Jan 2017 Caner Sahin, Rigas Kouskouridas, Tae-Kyun Kim

The iterative refinement is accomplished based on finer (smaller) parts that are represented with more discriminative control point descriptors by using our Iterative Hough Forest.

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

no code implementations8 Jul 2016 Andreas Doumanoglou, Vassileios Balntas, Rigas Kouskouridas, Tae-Kyun Kim

Furthermore, we argue that our pose-guided feature learning using our Siamese Regression Network generates more discriminative features that outperform the state of the art.

3D Pose Estimation Object +1

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

no code implementations8 Mar 2016 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.

Pose Estimation

Latent-Class Hough Forests for 6 DoF Object Pose Estimation

no code implementations3 Feb 2016 Rigas Kouskouridas, Alykhan Tejani, Andreas Doumanoglou, Danhang Tang, Tae-Kyun Kim

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.

object-detection Object Detection +2

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

no code implementations CVPR 2016 Andreas Doumanoglou, Rigas Kouskouridas, Sotiris Malassiotis, Tae-Kyun Kim

In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly.

6D Pose Estimation 6D Pose Estimation using RGB +4

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