Weak6D: Weakly Supervised 6D Pose Estimation With Iterative Annotation Resolver

Mu, Fengjun; Huang, Rui; Shi, Kecheng; Li, Xin; Qiu, Jing; Cheng, Hong

doi:10.1109/lra.2022.3190094

Cited by 3 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pose estimation aims at determining the 3D position and attitude of an object. Pose estimation plays a significant role in the field of computer vision and has applications in controlling a mechanical arm to grasp objects, robot navigation, augmented reality (AR), and other aspects 1 . Pose estimation is finding the conversion relationship between a set of 3D points in the world coordinate system and their corresponding 2D points in the image coordinate system, which will be of great benefit to us in locating objects in other applications.…”

Section: Introductionmentioning

confidence: 99%

“…Pose estimation plays a significant role in the field of computer vision and has applications in controlling a mechanical arm to grasp objects, robot navigation, augmented reality (AR), and other aspects. 1 Pose estimation is finding the conversion relationship between a set of 3D points in the world coordinate system and their corresponding 2D points in the image coordinate system, which will be of great benefit to us in locating objects in other applications. In 3D space, the position information can be expressed by the coordinates in the 3D coordinate system, whereas the attitude information can be expressed by matrices, quaternions, axial angles, and Euler angles.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-accuracy pose estimation algorithm with monocular camera based on residual network

You

Sun

2023

Opt. Eng.

View full text Add to dashboard Cite

Pose estimation is a typical problem encountered in computer vision. Therefore, it is important to improve the accuracy of this method. Aiming at the accuracy of pose estimation, we propose a high-accuracy pose estimation algorithm based on the you only look once network and residual network with a monocular camera as the sensor for visual acquisition. This algorithm uses ArUco markers as a reference for object localization and uses the red, green, and blue (RGB) image as the input. The input image is sampled 16 and 32 times to extract the feature image, and the feature image extracted by 16 times sampling is passed through the pass-through layer and then combined with the feature image extracted by 32 times sampling to accomplish the dimension expansion. The feature image is identified by the convolutional layer. The EPnP algorithm is used to solve the camera poses. The pose information of the target object in the RGB image is used as the output. By comparing the pose estimation accuracy for the LINEMOD dataset with three evaluation metrics-the 2D projection metric, ADD metric, and 5 cm to 5 deg metric-it can be observed that the pose estimation algorithm proposed has advantages in terms of accuracy compared with traditional pose estimation algorithms. When the target is very similar to the background objects, the algorithm also achieves good performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%