EPOS: Estimating 6D Pose of Objects With Symmetries

Hodaň, Tomáš; Baráth, Dániel; Matas, Jiřı́

doi:10.1109/cvpr42600.2020.01172

Cited by 199 publications

(149 citation statements)

References 57 publications

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“…The remaining three objects (cup, and black and white ducks) were discarded from the testing phase because they proved to be unrecognizable to the pose estimation algorithm. The cup model was discarded due to rotational symmetry, which is a common problem in pose estimation applications [34][35][36]. The pose estimation algorithm did not predict any meaningful results for the cup model (except for the elevation angle).…”

Section: D Pose Estimation Resultsmentioning

confidence: 99%

Geometric Change Detection in Digital Twins

Tiril

Maria

Rasheed

et al. 2021

Digital

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Digital twins are meant to bridge the gap between real-world physical systems and virtual representations. Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica. Digital twin applications are required to rapidly update internal parameters with the evolution of their physical counterpart. Due to an essential need for having high-quality geometric models for accurate physical representations, the storage and bandwidth requirements for storing 3D model information can quickly exceed the available storage and bandwidth capacity. In this work, we demonstrate a novel approach to geometric change detection in a digital twin context. We address the issue through a combined solution of dynamic mode decomposition (DMD) for motion detection, YOLOv5 for object detection, and 3D machine learning for pose estimation. DMD is applied for background subtraction, enabling detection of moving foreground objects in real-time. The video frames containing detected motion are extracted and used as input to the change detection network. The object detection algorithm YOLOv5 is applied to extract the bounding boxes of detected objects in the video frames. Furthermore, we estimate the rotational pose of each object in a 3D pose estimation network. A series of convolutional neural networks (CNNs) conducts feature extraction from images and 3D model shapes. Then, the network outputs the camera orientation’s estimated Euler angles concerning the object in the input image. By only storing data associated with a detected change in pose, we minimize necessary storage and bandwidth requirements while still recreating the 3D scene on demand. Our assessment of the new geometric detection framework shows that the proposed methodology could represent a viable tool in emerging digital twin applications.

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Section: D Pose Estimation Resultsmentioning

confidence: 99%

Geometric Change Detection in Digital Twins

Tiril

Maria

Rasheed

et al. 2021

Digital

View full text Add to dashboard Cite

show abstract

“…For point correspondence-based methods, we used the scoring coming from the standard SNN ratio-test [44]. For 6D object pose estimation, the points are ordered by their confidence values provided by deeplearning [45] in the used datasets.…”

Section: Gc-ransacmentioning

confidence: 99%

“…It includes 1000 test images from 20 scenes with varying complexity, including challenging scenes with multiple instances of several objects and with a high amount of clutter and occlusion. To get 2D-3D correspondences, we applied the EPOS method [45]. The tested robust estimators were applied to the obtained correspondences and the 6D pose was compared to the ground truth one.…”

Section: D Object Pose Estimationmentioning

confidence: 99%

Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures

Baráth

Matas

2021

IEEE Trans. Pattern Anal. Mach. Intell.

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We propose Graph-Cut RANSAC, GC-RANSAC in short, a new robust geometric model estimation method where the local optimization step is formulated as energy minimization with binary labeling, applying the graph-cut algorithm to select inliers. The minimized energy reflects the assumption that geometric data often form spatially coherent structures -it includes both a unary component representing point-to-model residuals and a binary term promoting spatially coherent inlier-outlier labelling of neighboring points. The proposed local optimization step is conceptually simple, easy to implement, efficient with a globally optimal inlier selection given the model parameters. Graph-Cut RANSAC, equipped with "the bells and whistles" of USAC and MAGSAC++, was tested on a range of problems using a number of publicly available datasets for homography, 6D object pose, fundamental and essential matrix estimation. It is more geometrically accurate than state-of-the-art robust estimators, fails less often and runs faster or with speed similar to less accurate alternatives.

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“…The pose estimation of symmetric objects is currently the most challenging task [11]. Existing multiple correct poses for the same visual appearance [12].…”

Section: Introductionmentioning

confidence: 99%

A Novel Distribution for Representation of 6D Pose Uncertainty

2022

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The 6D Pose estimation is a crux in many applications, such as visual perception, autonomous navigation, and spacecraft motion. For robotic grasping, the cluttered and self-occlusion scenarios bring new challenges to the this field. Currently, society uses CNNs to solve this problem. The CNN models will suffer high uncertainty caused by the environmental factors and the object itself. These models usually maintain a Gaussian distribution, which is not suitable for the underlying manifold structure of the pose. Many works decouple rotation from the translation and quantify rotational uncertainty. Only a few works pay attention to the uncertainty of the 6D pose. This work proposes a distribution that can capture the uncertainty of the 6D pose parameterized by the dual quaternions, meanwhile, the proposed distribution takes the periodic nature of the underlying structure into account. The presented results include the normalization constant computation and parameter estimation techniques of the distribution. This work shows the benefits of the proposed distribution, which provides a more realistic explanation for the uncertainty in the 6D pose and eliminates the drawback inherited from the planar rigid motion.

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EPOS: Estimating 6D Pose of Objects With Symmetries

Cited by 199 publications

References 57 publications

Geometric Change Detection in Digital Twins

Geometric Change Detection in Digital Twins

Graph-Cut RANSAC: Local Optimization on Spatially Coherent Structures

A Novel Distribution for Representation of 6D Pose Uncertainty

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