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 )
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.
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.
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.
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Estimating the 6D pose of objects using only RGB images remains challenging because of problems such as occlusion and symmetries.
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).
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.
BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
We introduce a novel method for 3D object detection and pose estimation from color images only.