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 )
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We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation.
Ranked #1 on 6D Pose Estimation using RGB on YCB-Video (Mean AUC metric)
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.
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.
Ranked #2 on 6D Pose Estimation using RGB on YCB-Video
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.
Ranked #1 on 6D Pose Estimation using RGB on OCCLUSION
We introduce a novel method for 3D object detection and pose estimation from color images only.
Ranked #13 on 6D Pose Estimation using RGB on LineMOD
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).
Ranked #2 on 6D Pose Estimation using RGB on Occlusion LineMOD
We present a novel method for detecting 3D model instances and estimating their 6D poses from RGB data in a single shot.
Ranked #1 on 6D Pose Estimation using RGBD on Tejani
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Ranked #1 on 6D Pose Estimation using RGBD on CAMERA25
Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization.
Ranked #1 on 6D Pose Estimation using RGB on T-LESS (Mean Recall metric)
Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality.
Ranked #1 on 6D Pose Estimation using RGB on Occlusion LineMOD