Partial and approximate symmetry detection for 3D geometry

By Niloy J. Mitra, Leonidas J. Guibas, Mark Pauly. 2006

649 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

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In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Learning Shape Priors for Single-View 3D Completion and Reconstruction (2018, background): “…our ShapeHD aims to model ambiguity on the next level: even among the realistic shapes, there are still multiple shapes explaining the observation well (c). growing number of papers on exploiting shape structures and regularities [34,51], and papers on leveraging strong database priors [46,32,4].”
Shape Completion Using 3D-Encoder-Predictor CNNs and Shape Synthesis (2016, background): “For instance, many works detect symmetries in meshes or point clouds, and use them to ﬁll in missing data [41, 23, 30, 36, 39].”
Completing 3D object shape from one depth image (2015, background): “We find the significant symmetries in the retrieved mesh using [27].”
Discovering Structural Regularity in 3D Geometry (2008, methodology): “This example also highlights the difference to top-down symmetry detection methods (cf. Figure 7 of [Mitra et al. 2006]).”
Extracting lines of curvature from noisy point clouds (2009, methodology): “Surface derivatives such as normal and curvature are as important as surface position for the perception, understanding, interrogation of shape (30; 26), registration (42), smoothing (27) and symmetry detection (34) to name a few applications.”
Shape Completion from a Single RGBD Image (2017, methodology): “To support symmetry constraints, we perform symmetry analysis [31] to detect re ective and rotational symmetries in each model.”
ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2017, methodology): “Other shape completion methods have been developed, including approaches that leverage symmetries in meshes or point clouds [40, 19, 26, 34, 37] or part-based structural priors derived from a database [38].”
3D Object Dense Reconstruction from a Single Depth View (2018, methodology): “[25] uses plane fitting to complete small missing regions, while [26] [27] [28] [29] [30] apply shape symmetry to fill in holes.”
3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, positive): “[35] uses plane fitting to complete small missing regions, while [32] [34] [40] [48] [52] [56] applies shape symmetry to fill in holes.”
PCN: Point Completion Network (2018, methodology): “Symmetry-driven methods [29, 30, 35, 36, 46, 52, 55] identify symmetry axes and repeating regular structures in the partial input in order to copy parts from observed regions to unobserved regions.”
Curve skeleton extraction from incomplete point cloud (2009, background): “There has been a great deal of recent research on symmetry detection and symmetry-aware geometry processing, e.g., [Kazhdan et al. 2004; Mitra et al. 2006; Podolak et al. 2006; Ovsjanikov et al. 2008], to name a few.”
Data-driven structural priors for shape completion (2015, background): “, [Pauly et al. 2008; Mitra et al. 2006]) infer symmetry from the input shape, which means that they need to observe at least some fraction of the occluded counter-part to detect the symmetry.”