Plane Space Representation in Context of Mode-Based Symmetry Plane Detection

Hruda, Lukáš; Kolingerová, Ivana; Lávička, Miroslav

doi:10.1007/978-3-030-50426-7_38

Cited by 4 publications

(1 citation statement)

References 15 publications

(27 reference statements)

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“…This leads an increased computational complexity. Hruda et al proposed an efficient method where they proposed a differential measure for reflection symmetry in 3D point clouds [37], [38]. However, the performance of this method does not perform well for non-uniformly sampled point clouds.…”

Section: Related Workmentioning

confidence: 99%

Robust Estimation of Reflection Symmetry in Noisy and Partial 3D Point Clouds

Nagar¹

2021

Preprint

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Detecting the reflection symmetry plane of an object represented by a 3D point cloud is a fundamental problem in 3D computer vision and geometry processing due to its various applications such as compression, object detection, robotic grasping, 3D surface reconstruction, etc. There exist several efficient approaches for solving this problem for clean 3D point clouds. However, this problem becomes difficult to solve in the presence of outliers and missing parts due to occlusions while scanning the objects through 3D scanners. The existing methods try to overcome these challenges mostly by voting-based techniques but fail in challenging settings. In this work, we propose a statistical estimator for the plane of reflection symmetry that is robust to outliers and missing parts. We pose the problem of finding the optimal estimator as an optimization problem on a 2-sphere that quickly converges to the global solution. We further propose a 3D point descriptor that is invariant to 3D reflection symmetry using the spectral properties of the geodesic distance matrix constructed from the neighbors of a point. This helps us in decoupling the chicken-and-egg problem of finding optimal symmetry plane and correspondences between the reflective symmetric points. We show that the proposed approach achieves the state-of-the-art performance on the benchmarks dataset.

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Section: Related Workmentioning

confidence: 99%

Robust Estimation of Reflection Symmetry in Noisy and Partial 3D Point Clouds

Nagar¹

2021

Preprint

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Symmetry-based decomposition for optimised parallelisation in 3D printing processes

Hatton

Khalid

Manzoor

et al. 2023

Int J Adv Manuf Technol

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Current research in 3D printing focuses on improving printing performance through various techniques, including decomposition, but targets only single printers. With improved hardware costs increasing printer availability, more situations can arise involving a multitude of printers, which offers substantially more throughput in combination that may not be best utilised by current decomposition approaches. A novel approach to 3D printing is introduced that attempts to exploit this as a means of significantly increasing the speed of printing models. This was approached as a problem akin to the parallel delegation of computation tasks in a multi-core environment, where optimal performance involves computation load being distributed as evenly as possible. To achieve this, a decomposition framework was designed that combines recursive symmetric slicing with a hybrid tree-based analytical and greedy strategy to optimally minimise the maximum volume of subparts assigned to the set of printers. Experimental evaluation of the algorithm was performed to compare our approach to printing models normally (“in serial”) as a control. The algorithm was subjected to a range of models and a varying quantity of printers in parallel, with printer parameters held constant, and yielded mixed results. Larger, simpler, and more symmetric objects exhibited more significant and reliable improvements in fabrication duration at larger amounts of parallelisation than smaller, more complex, or more asymmetric objects.

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