6D Pose Estimation using RGB

55 papers with code • 6 benchmarks • 4 datasets

6D pose estimation is the task of detecting the 6D pose of an object, which include its location and orientation. This is an important task in robotics, where a robotic arm needs to know the location and orientation to detect and move objects in its vicinity successfully. This allows the robot to operate safely and effectively alongside humans. The awareness of the position and orientation of objects in a scene is sometimes referred to as 6D, where the D stands for degrees of freedom pose.

( Image credit: Segmentation-driven 6D Object Pose Estimation )

Libraries

Use these libraries to find 6D Pose Estimation using RGB models and implementations

Most implemented papers

PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

yuxng/PoseCNN 1 Nov 2017

We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.

Real-Time Seamless Single Shot 6D Object Pose Prediction

Microsoft/singleshotpose CVPR 2018

For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches when they are all used without post-processing.

Estimating 6D Pose From Localizing Designated Surface Keypoints

sjtuytc/betapose 4 Dec 2018

In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image.

Segmentation-driven 6D Object Pose Estimation

cvlab-epfl/segmentation-driven-pose CVPR 2019

The most recent trend in estimating the 6D pose of rigid objects has been to train deep networks to either directly regress the pose from the image or to predict the 2D locations of 3D keypoints, from which the pose can be obtained using a PnP algorithm.

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

hughw19/NOCS_CVPR2019 CVPR 2019

The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.

Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation

kirumang/Pix2Pose ICCV 2019

Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries.

HybridPose: 6D Object Pose Estimation under Hybrid Representations

chensong1995/HybridPose CVPR 2020

Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e. g., because of occlusion).

BOP Challenge 2020 on 6D Object Localization

thodan/bop_toolkit 15 Sep 2020

This paper presents the evaluation methodology, datasets, and results of the BOP Challenge 2020, the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image.

T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects

thodan/t-less_toolkit 19 Jan 2017

There are approximately 39K training and 10K test images from each sensor.