Shape from symmetry

By Sebastian Thrun, Ben Wegbreit. 2005

268 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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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].”
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.”
A Symmetry Prior for Convex Variational 3D Reconstruction (2016, methodology): “Application-wise, the work by Thrun and Wegbreit [26] is closely related to ours.”
VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion (2023, background): “Another line of research is 3D semantic scene completion, unifying scene completion and semantic segmentation which are investigated separately at the early stage [50,51].”
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].”
Structured Prediction of Unobserved Voxels from a Single Depth Image (2016, background): “Symmetry can be leveraged to complete some types of objects, e.g. [38] [58] [35].”
A planar-reflective symmetry transform for 3D shapes (2006, background): “…local features in 2D images [Reisfeld et al. 1995], guide reconstruction of 2D curves and range scans with missing data [Zabrodsky et al. 1993; Thrun and Wegbreit 2005], rotate shapes into a canonical coordinate frame [Kazhdan et al. 2003a], match the shapes of meshes [Kazhdan et al. 2004],…”
3-D Depth Reconstruction from a Single Still Image (2007, background): “A large class of algorithms reconstruct the 3-d shape of known objects, such as human bodies, from images and laser data [8; 9].”
Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the Wild (2019, background): “In fact, if correspondences between the pair of mirrored images were available, 3D reconstruction could be achieved by stereo reconstruction [38, 11, 56, 50, 13].”
Morphing and Sampling Network for Dense Point Cloud Completion (2019, background): “2002; Zhao, Gao, and Lin 2007) or rely on some geometric assumptions, such as symmetry (Thrun and Wegbreit 2005; Sipiran, Gregor, and Schreck 2014).”
3D Object Reconstruction from a Single Depth View with Adversarial Learning (2017, unknown): “[35] uses plane fitting to complete small missing regions, while [32] [34] [40] [48] [52] [56] applies shape symmetry to fill in holes.”
Learning 3D Shape Completion from Laser Scan Data with Weak Supervision (2018, background): “The former leverage observed symmetry to complete shapes; representative works include [27, 29, 34, 46, 51].”
3D Shape Perception from Monocular Vision, Touch, and Shape Priors (2018, unknown): “Recently, there have also been a growing number of papers on completing shapes via their structure and regularities [6] and on learning shape priors in a fully data-driven style [7].”
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.”