Estimation of 6D Object Pose Using a 2D Bounding Box
This paper provides an efficient way of addressing the problem of detecting or estimating the 6-Dimensional (6D) pose of objects from an RGB image. A quaternion is used to define an object′s three-dimensional pose, but the pose represented by q and the pose represented by -q are equivalent, and the...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/9/2939 |
| _version_ | 1797536748054511616 |
|---|---|
| author | Yong Hong Jin Liu Zahid Jahangir Sheng He Qing Zhang |
| author_facet | Yong Hong Jin Liu Zahid Jahangir Sheng He Qing Zhang |
| author_sort | Yong Hong |
| collection | DOAJ |
| description | This paper provides an efficient way of addressing the problem of detecting or estimating the 6-Dimensional (6D) pose of objects from an RGB image. A quaternion is used to define an object′s three-dimensional pose, but the pose represented by q and the pose represented by -q are equivalent, and the L2 loss between them is very large. Therefore, we define a new quaternion pose loss function to solve this problem. Based on this, we designed a new convolutional neural network named Q-Net to estimate an object’s pose. Considering that the quaternion′s output is a unit vector, a normalization layer is added in Q-Net to hold the output of pose on a four-dimensional unit sphere. We propose a new algorithm, called the Bounding Box Equation, to obtain 3D translation quickly and effectively from 2D bounding boxes. The algorithm uses an entirely new way of assessing the 3D rotation (R) and 3D translation rotation (t) in only one RGB image. This method can upgrade any traditional 2D-box prediction algorithm to a 3D prediction model. We evaluated our model using the LineMod dataset, and experiments have shown that our methodology is more acceptable and efficient in terms of L2 loss and computational time. |
| first_indexed | 2024-03-10T12:05:11Z |
| format | Article |
| id | doaj.art-e4646fa4145c4590aa784ba387e531eb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T12:05:11Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e4646fa4145c4590aa784ba387e531eb2023-11-21T16:40:06ZengMDPI AGSensors1424-82202021-04-01219293910.3390/s21092939Estimation of 6D Object Pose Using a 2D Bounding BoxYong Hong0Jin Liu1Zahid Jahangir2Sheng He3Qing Zhang4State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaThis paper provides an efficient way of addressing the problem of detecting or estimating the 6-Dimensional (6D) pose of objects from an RGB image. A quaternion is used to define an object′s three-dimensional pose, but the pose represented by q and the pose represented by -q are equivalent, and the L2 loss between them is very large. Therefore, we define a new quaternion pose loss function to solve this problem. Based on this, we designed a new convolutional neural network named Q-Net to estimate an object’s pose. Considering that the quaternion′s output is a unit vector, a normalization layer is added in Q-Net to hold the output of pose on a four-dimensional unit sphere. We propose a new algorithm, called the Bounding Box Equation, to obtain 3D translation quickly and effectively from 2D bounding boxes. The algorithm uses an entirely new way of assessing the 3D rotation (R) and 3D translation rotation (t) in only one RGB image. This method can upgrade any traditional 2D-box prediction algorithm to a 3D prediction model. We evaluated our model using the LineMod dataset, and experiments have shown that our methodology is more acceptable and efficient in terms of L2 loss and computational time.https://www.mdpi.com/1424-8220/21/9/29396D pose estimationquaternionBounding Box EquationLineMod |
| spellingShingle | Yong Hong Jin Liu Zahid Jahangir Sheng He Qing Zhang Estimation of 6D Object Pose Using a 2D Bounding Box Sensors 6D pose estimation quaternion Bounding Box Equation LineMod |
| title | Estimation of 6D Object Pose Using a 2D Bounding Box |
| title_full | Estimation of 6D Object Pose Using a 2D Bounding Box |
| title_fullStr | Estimation of 6D Object Pose Using a 2D Bounding Box |
| title_full_unstemmed | Estimation of 6D Object Pose Using a 2D Bounding Box |
| title_short | Estimation of 6D Object Pose Using a 2D Bounding Box |
| title_sort | estimation of 6d object pose using a 2d bounding box |
| topic | 6D pose estimation quaternion Bounding Box Equation LineMod |
| url | https://www.mdpi.com/1424-8220/21/9/2939 |
| work_keys_str_mv | AT yonghong estimationof6dobjectposeusinga2dboundingbox AT jinliu estimationof6dobjectposeusinga2dboundingbox AT zahidjahangir estimationof6dobjectposeusinga2dboundingbox AT shenghe estimationof6dobjectposeusinga2dboundingbox AT qingzhang estimationof6dobjectposeusinga2dboundingbox |