ContourPose: Monocular 6-D Pose Estimation Method for Reflective Textureless Metal Parts

He, Zaixing; Li, Quanzhi; Zhao, Xinyue; Wang, Jin; Shen, Hong; Zhang, Shuyou; Tan, Jianrong

doi:10.1109/tro.2023.3290300

Cited by 4 publications

(2 citation statements)

References 42 publications

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“…Many existing datasets for estimating the pose of 6D objects have limitations, which make them less applicable to industrial scenarios. The Benchmark for 6D Object Pose Estimation (BOP) [7] unifies 12 datasets, of which the majority focus on household objects, which differ significantly from industrial ones [14]. Household objects are often non-symmetric with diverse textures, making them easier to identify and position.…”

Section: Introductionmentioning

confidence: 99%

Sim-to-Real Dataset of Industrial Metal Objects

De Roovere,

Moonen,

Michiels

et al. 2024

Machines

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We present a diverse dataset of industrial metal objects with unique characteristics such as symmetry, texturelessness, and high reflectiveness. These features introduce challenging conditions that are not captured in existing datasets. Our dataset comprises both real-world and synthetic multi-view RGB images with 6D object pose labels. Real-world data were obtained by recording multi-view images of scenes with varying object shapes, materials, carriers, compositions, and lighting conditions. This resulted in over 30,000 real-world images. We introduce a new public tool that enables the quick annotation of 6D object pose labels in multi-view images. This tool was used to provide 6D object pose labels for all real-world images. Synthetic data were generated by carefully simulating real-world conditions and varying them in a controlled and realistic way. This resulted in over 500,000 synthetic images. The close correspondence between synthetic and real-world data and controlled variations will facilitate sim-to-real research. Our focus on industrial conditions and objects will facilitate research on computer vision tasks, such as 6D object pose estimation, which are relevant for many industrial applications, such as machine tending. The dataset and accompanying resources are available on the project website.

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Section: Introductionmentioning

confidence: 99%

Sim-to-Real Dataset of Industrial Metal Objects

De Roovere,

Moonen,

Michiels

et al. 2024

Machines

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“…The cheap RGB-D cameras usually cannot get high-precision depth images when sensing shiny metal parts. Therefore, pose estimation of metal parts with RGB images has raised attention [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-View Metal Parts Pose Estimation Based on a Single Camera

Chen,

Jiang

2024

Sensors

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Pose estimation of metal parts plays a vital role in industrial grasping areas. It is challenging to obtain complete point clouds of metal parts because of their reflective properties. This study introduces an approach for recovering the 6D pose of CAD-known metal parts from images captured by a single RGB camera. The proposed strategy only requires RGB images without depth information. The core idea of the proposed method is to use multiple views to estimate the metal parts’ pose. First, the pose of metal parts is estimated in the first view. Second, ray casting is employed to simulate additional views with the corresponding status of the metal parts, enabling the calculation of the camera’s next best viewpoint. The camera, mounted on a robotic arm, is then moved to this calculated position. Third, this study integrates the known camera transformations with the poses estimated from different viewpoints to refine the final scene. The results of this work demonstrate that the proposed method effectively estimates the pose of shiny metal parts.

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Yolo-S: Texture-Less Object Pose Estimation with Shape Prior

Wang,

Liu

2024

2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS)

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ContourPose: Monocular 6-D Pose Estimation Method for Reflective Textureless Metal Parts

Cited by 4 publications

References 42 publications

Sim-to-Real Dataset of Industrial Metal Objects

Sim-to-Real Dataset of Industrial Metal Objects

Multi-View Metal Parts Pose Estimation Based on a Single Camera

Yolo-S: Texture-Less Object Pose Estimation with Shape Prior

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