Robust Monocular Pose Tracking of Less-Distinct Objects Based on Contour-Part Model

Sun, Xiaoliang; Zhou, Jiexin; Zhang, Wenlong; Wang, Zi; Yu, Qifeng

doi:10.1109/tcsvt.2021.3053696

Cited by 19 publications

(22 citation statements)

References 37 publications

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“…The sequence also contains rapid rotational change when the camera rotates axially along with the grasping hand, which brings more challenges for robust pose tracking. From the reprojected results, we find that edge-based methods 18,22 tend to fail in tracking the pose when the object scale starts to shrink. TPAMI19 17 keeps tracking successfully until the object scale decreases to the minimum and starts to fail when the object rotates in its smallest scale.…”

Section: Evaluation On the Synthetic Image Sequencesmentioning

confidence: 99%

“…To validate the performance of the proposed method, experiments on synthetic and real image sequences are conducted. We compared our method with the representative region-based method TPAMI19 17 and edge-based methods (VC15 22 , TCSVT21 18 ). For all the methods to be compared, we adopt the parameters suggested by the authors directly.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…For qualitative evaluation, we compare the proposed method with the studies of Tjaden et al, Sun et al, and Wang et al 17,18,22 Figure 13 shows the sampled reprojected tracking results on the synthetic image sequence of phone.…”

Section: Evaluation On the Synthetic Image Sequencesmentioning

confidence: 99%

“…CAD model), the object appearance information is also employed, including texture, templates, keypoints mapped on the object surface, and so on. 18 Specifically, depending on the difference of the image information used, existing monocular pose tracking methods can be classified into five major categories 19 : direct methods, feature-based methods, edge-based methods, region-based methods, and deep learning based methods.…”

Section: Introductionmentioning

confidence: 99%

“…This kind of methods can easily fall into local minima and easily fail in tracking heterogeneous objects. 18,21,22 Recently, regionbased pose tracking methods 17,23,24 have gained increasing popularity and achieved state-of-the-art performances. Region-based methods unite pose estimation and image segmentation with the 3D model simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Robust monocular 3D object pose tracking for large visual range variation in robotic manipulation via scale-adaptive region-based method

Zhou

Wang

Bao

et al. 2022

International Journal of Advanced Robotic Systems

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Many robot manipulation processes involve large visual range variation between the hand-eye camera and the object, which in turn causes object scale change of a large span in the image sequence captured by the camera. In order to accurately guide the manipulator, the relative 6 degree of freedom (6D) pose between the object and manipulator is continuously required in the process. The large-span scale change of the object in the image sequence often leads to the 6D pose tracking failure of the object for existing pose tracking methods. To tackle this problem, this article proposes a novel scale-adaptive region-based monocular pose tracking method. Firstly, the impact of the object scale on the convergence performance of the local region-based pose tracker is meticulously tested and analyzed. Then, a universal region radius calculation model based on object scale is built based on the statical analysis result. Finally, we develop a novel scale-adaptive localized region-based pose tracking model by merging the scale-adaptive radius selection mechanism into the local region-based method. The proposed method adjusts local region size according to the scale of the object projection and achieves robust pose tracking. Experiment results on synthetic and real image sequences indicate that the proposed method achieves better performance over the traditional localized region-based method in manipulator operation scenarios which involve large visual range variation.

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Section: Evaluation On the Synthetic Image Sequencesmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

Section: Evaluation On the Synthetic Image Sequencesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Robust monocular 3D object pose tracking for large visual range variation in robotic manipulation via scale-adaptive region-based method

Zhou

Wang

Bao

et al. 2022

International Journal of Advanced Robotic Systems

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A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

Stoiber

Pfanne

Strobl

et al. 2021

Lecture Notes in Computer Science

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In many applications of advanced robotic manipulation, six degrees of freedom (6DoF) object pose estimates are continuously required. In this work, we develop a multimodality tracker that fuses information from visual appearance and geometry to estimate object poses. The algorithm extends our previous method ICG, which uses geometry, to additionally consider surface appearance. In general, object surfaces contain local characteristics from text, graphics, and patterns, as well as global differences from distinct materials and colors. To incorporate this visual information, two modalities are developed. For local characteristics, keypoint features are used to minimize distances between points from keyframes and the current image. For global differences, a novel region approach is developed that considers multiple regions on the object surface. In addition, it allows the modeling of external geometries. Experiments on the YCB-Video and OPT datasets demonstrate that our approach ICG+ performs best on both datasets, outperforming both conventional and deep learningbased methods. At the same time, the algorithm is highly efficient and runs at more than 300 Hz. The source code of our tracker is publicly available.

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