Seeing Glassware: from Edge Detection to Pose Estimation and Shape Recovery

Phillips, Cody; Lecce, Matthieu; Daniilidis, Kostas

doi:10.15607/rss.2016.xii.021

Cited by 43 publications

(21 citation statements)

References 29 publications

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“…Current robot vision systems have become possible to recognize mirrors and transparent objects using aformentioned previous methods. Robot manipulators have been able to grasp them and mobile robots could avoid them [24]- [27]. Recent studies have used deep learning to extract latent characteristics of the appearance of transparent objects from large amounts of training data [28]- [32].…”

Section: Related Work a Vision-based Approachmentioning

confidence: 99%

Active Vapor-Based Robotic Wiper

Kiyokawa¹,

Katayama²,

Takamatsu³

2021

Preprint

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This paper presents a method of normal estimation for mirrors and transparent objects, which are difficult to recognize with a camera. To create a diffuse reflective surface, we propose to spray the water vapor onto the transparent or mirror surface. In the proposed method, we move an ultrasonic humidifier equipped on the tip of a robotic arm to apply the sprayed water vapor onto a plane of a target object so as to form a cross-shaped misted area. Diffuse reflective surfaces are partially generated as the misted area, which allows the camera to detect a surface of the target object. The viewpoint of the gripper-mounted camera is adjusted so that the extracted misted area appears as largest in the image, and finally the plane normal of the target object surface are estimated. We conducted normal estimation experiments to evaluate the effectiveness of the proposed method. The RMSEs of the azimuth estimation for a mirror and a transparent glass are about 4.2 and 5.8 degrees, respectively. Consequently, our robot experiments demonstrate that our robotic wiper can perform contact-force-regulated wiping motions for cleaning a transparent window as humans do.

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Section: Related Work a Vision-based Approachmentioning

confidence: 99%

Active Vapor-Based Robotic Wiper

Kiyokawa¹,

Katayama²,

Takamatsu³

2021

Preprint

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“…It is worth noting that other sensors can also be used for transparent object pose estimation. For example, transparent object pose can be estimated through a monocular color camera [43,44], but the translation estimation along the z-axis tends to be inaccurate due to lack of 3D depth information. Stereo camera [45,46], light field camera [47], single pixel camera [48], and microscope-camera system [49] can be used for object pose estimation, but these works are very different from this paper and are not discussed further.…”

Section: Related Workmentioning

confidence: 99%

6DoF Pose Estimation of Transparent Object from a Single RGB-D Image

Chen

Yao

et al. 2020

Sensors

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6DoF object pose estimation is a foundation for many important applications, such as robotic grasping, automatic driving, and so on. However, it is very challenging to estimate 6DoF pose of transparent object which is commonly seen in our daily life, because the optical characteristics of transparent material lead to significant depth error which results in false estimation. To solve this problem, a two-stage approach is proposed to estimate 6DoF pose of transparent object from a single RGB-D image. In the first stage, the influence of the depth error is eliminated by transparent segmentation, surface normal recovering, and RANSAC plane estimation. In the second stage, an extended point-cloud representation is presented to accurately and efficiently estimate object pose. As far as we know, it is the first deep learning based approach which focuses on 6DoF pose estimation of transparent objects from a single RGB-D image. Experimental results show that the proposed approach can effectively estimate 6DoF pose of transparent object, and it out-performs the state-of-the-art baselines by a large margin.

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“…In view-based methods, the comparison result among precomputed 2D views of the object and the query image defines the position of the object. To accelerate the running time in these methods, the similar viewpoints will be merged [15,17,24,28,36]. Some of the view-based methods fixed the distance between the camera and the object (like the object exists on a table or conveyor belt) with a known constant distance from the camera.…”

Section: Related Workmentioning

confidence: 99%

Object Pose Estimation in Monocular Image Using Modified FDCM

Dabbour

Habib

Saii

2020

csci

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In this paper, a new method for object detection and pose estimation in a monocular image is proposed based on FDCM method. it can detect object with high speed running time, even if the object was under the partial occlusion or in bad illumination. In addition, It requires only single template without any training process. The Modied FDCM based on FDCM with improvments, the LSD method was used in MFDCM instead of the line tting method, besides the integral distance transform was replaced with a distance transform image, and using an angular Voronoi diagram. In addition, the search process depends on Line segments based search instead of the sliding window search in FDCM. The MFDCM was evaluated by comparing it with FDCM in dierent scenarios and with other four methods: COF, HALCON, LINE2D, and BOLD using D-textureless dataset. The comparison results show that MFDCM was at least 14 times faster than FDCM in tested scenarios. Furthermore, it has the highest correct detection rate among all tested method with small advantage from COF and BLOD methods, while it was a little slower than LINE2D which was the fasted method among compared methods. The results proves that MFDCM able to detect and pose estimation of the objects in the clear or clustered background from a monocular image with high speed running time, even if the object was under the partial occlusion which makes it robust and reliable for real-time applications.

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Seeing Glassware: from Edge Detection to Pose Estimation and Shape Recovery

Cited by 43 publications

References 29 publications

Active Vapor-Based Robotic Wiper

Active Vapor-Based Robotic Wiper

6DoF Pose Estimation of Transparent Object from a Single RGB-D Image

Object Pose Estimation in Monocular Image Using Modified FDCM

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