Efficient 3D reflection symmetry detection: A view-based approach

Li, Bo; Johan, Henry; Ye, Yuxiang; Lu, Yijuan

doi:10.1016/j.gmod.2015.09.003

Cited by 31 publications

(26 citation statements)

References 47 publications

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“…Podolak et al [2] further describe a planar reflective symmetry transform (PRST) that captures a continuous measure to help find the reflective symmetry. Li et al [20] present a detection method based on the viewpoint entropy features. Ecins et al [21] introduce a method that mainly detects symmetric objects in 3D scenes and scans of real environment.…”

Section: Related Work 21 Symmetry Detectionmentioning

confidence: 99%

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

Gao

Zhang

Meng

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces.

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Section: Related Work 21 Symmetry Detectionmentioning

confidence: 99%

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

Gao

Zhang

Meng

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…is method can detect symmetries for objects with simple geometry in occluded tabletop scenes, but it is still limited by its inferior generality so cannot be extended to more general object types. Another 3D symmetry detection approach is to first predict the complete geometry of the input data [20] followed by a conventional symmetry detection [21]. e drawback is that it requires the shape completion method to make point-level predictions with high accuracy, which is nontrivial as the training data collection and network training procedures are both effort-intensive.…”

Section: D Symmetry Detection 3d Symmetry Detection Has Received Significant Research Attention In Computer Visionmentioning

confidence: 99%

Symmetry-Aware 6D Object Pose Estimation via Multitask Learning

Zhang

Fang

et al. 2020

Complexity

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Although 6D object pose estimation has been intensively explored in the past decades, the performance is still not fully satisfactory, especially when it comes to symmetric objects. In this paper, we study the problem of 6D object pose estimation by leveraging the information of object symmetry. To this end, a network is proposed that predicts 6D object pose and object reflectional symmetry as well as the key points simultaneously via a multitask learning scheme. Consequently, the pose estimation is aware of and regulated by the symmetry axis and the key points of the to-be-estimated objects. Moreover, we devise an optimization function to refine the predicted 6D object pose by considering the predicted symmetry. Experiments on two datasets demonstrate that the proposed symmetry-aware approach outperforms the existing methods in terms of predicting 6D pose estimation of symmetric objects.

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“…The detailed derivation is given in the Appendix §A6. Now, we apply the Riemannian-trust-region method using the Riemannian gradient and Hessian defined in Equations (12), (13), (14), and (15) in order to obtain the optimal solution. We use the manopt toolbox in order to implement the optimization problem given in Equation (5) for a fixed P [52].…”

Section: B Optimizing Reflection Transformation (R T)mentioning

confidence: 99%

“…Input: Set of points S = {x i } n i=1 . Initialize angles θ 0 and translation t. 3: Solve the ILP defined in Equation(17)for P. 4: For this P, solve for (R, t) using the Riemannian-trustregion method using the Riemannian gradient and Hessian defined in Equations(12),(13),(14), and (15). 5: Keep iterating steps 3 and 4 till convergence.…”

mentioning

confidence: 99%

Detecting Approximate Reflection Symmetry in a Point Set Using Optimization on Manifold

Nagar

Raman

2019

IEEE Trans. Signal Process.

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We propose an algorithm to detect approximate reflection symmetry present in a set of volumetrically distributed points belonging to R d containing a distorted reflection symmetry pattern. We pose the problem of detecting approximate reflection symmetry as the problem of establishing correspondences between the points which are reflections of each other and we determine the reflection symmetry transformation. We formulate an optimization framework in which the problem of establishing the correspondences amounts to solving a linear assignment problem and the problem of determining the reflection symmetry transformation amounts to solving an optimization problem on a smooth Riemannian product manifold. The proposed approach estimates the symmetry from the geometry of the points and is descriptor independent. We evaluate the performance of the proposed approach on the standard benchmark dataset and achieve the state-of-the-art performance. We further show the robustness of our approach by varying the amount of distortion in a perfect reflection symmetry pattern where we perturb each point by a different amount of perturbation. We demonstrate the effectiveness of the method by applying it to the problem of 2-D and 3-D reflection symmetry detection along with comparisons.

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Efficient 3D reflection symmetry detection: A view-based approach

Cited by 31 publications

References 47 publications

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

Symmetry-Aware 6D Object Pose Estimation via Multitask Learning

Detecting Approximate Reflection Symmetry in a Point Set Using Optimization on Manifold

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