Search Results for author: Caner Sahin

Found 10 papers, 1 papers with code

A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimators

no code implementations28 Jan 2020 Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim

In this paper, we present the first comprehensive and most recent review of the methods on object pose recovery, from 3D bounding box detectors to full 6D pose estimators.

6D Pose Estimation using RGB Autonomous Driving +2

Instance- and Category-level 6D Object Pose Estimation

no code implementations11 Mar 2019 Caner Sahin, Guillermo Garcia-Hernando, Juil Sock, Tae-Kyun Kim

6D object pose estimation is an important task that determines the 3D position and 3D rotation of an object in camera-centred coordinates.

6D Pose Estimation using RGB Scene Understanding

Category-level 6D Object Pose Recovery in Depth Images

no code implementations1 Aug 2018 Caner Sahin, Tae-Kyun Kim

Intra-class variations, distribution shifts among source and target domains are the major challenges of category-level tasks.

6D Pose Estimation using RGB Graph Matching

Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios

no code implementations11 Jun 2018 Juil Sock, Kwang In Kim, Caner Sahin, Tae-Kyun Kim

Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects.

3D Pose Estimation Multi-Task Learning

Recovering 6D Object Pose: A Review and Multi-modal Analysis

no code implementations10 Jun 2017 Caner Sahin, Tae-Kyun Kim

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality.

6D Pose Estimation 6D Pose Estimation using RGB +2

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.

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

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