A Level-Set Approach to 3D Reconstruction from Range Data

By Ross T. Whitaker. 1998

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Citations in the corpus

Deep Level Sets: Implicit Surface Representations for 3D Shape Inference (2019, methodology): “The level set method introduced in [8, 26], and successfully applied in segmentation and medical image analysis [18, 32], is a mathematically elegant way of implicitly representing shape of an object and its boundary evolution in time, which can be represented as a zero level set of an embedding…”
Minimizing the Reprojection Error in Surface Reconstruction from Images (2007, background): “Another example would be the reconstruction from noisy range images [ 24], where g would measure the difference between the captured depth at u and the depth of π − 1 Γ ( u ) .”
A quasi-dense approach to surface reconstruction from uncalibrated images (2005, methodology): “[64] has also been reported by Whitaker [ 60 ] using a MAP framework.”
An image-based modeling framework for patient-specific computational hemodynamics (2008, methodology): “All surfaces resulting from segmentation were aligned onto the STL surface using the iterative closest point (ICP) algorithm [32].”
3D distance fields: a survey of techniques and applications (2006, methodology): “Yet, another approach is the sparse field method proposed by [131] where a simplified reinitialization is an integral part of the method which keeps explicit lists of layers (ordered by distance) of adjacent grid points.”
The 3D Model Acquisition Pipeline (2002, unknown): “[73,74].”
Implicit Surface Representations As Layers in Neural Networks (2019, methodology): “The level set method introduced in [8, 27], and successfully applied in segmentation and medical image analysis [18, 33], is a mathematically elegant way of implicitly representing shape of an object and its boundary evolution in time, which can be represented as a zero level set of an embedding fun”
Geometric Level Set Methods in Imaging, Vision, and Graphics (2011, methodology): “Level set methods have been successfully applied in computer graphics, computer vision and visualization [476, 458], for example medical image segmentation [331, 574], shape morphing [67], 3D reconstruction [573, 609], and recently for the animation of liquids [198].”
Global Optimization for Shape Fitting (2007, result): “Our results for sparse or incomplete 3D data from laser scanning and passive multi-view stereo are robust to noise, outliers, missing parts, and varying sampling density.”
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability (2006, methodology): “SNAP uses the highly eﬃcient Extreme Narrow Banding Method by Whitaker (1998) to solve (5).”
Robust and Efficient Surface Reconstruction From Range Data (2009, methodology): “As seen in section 4.2, our method can still be used, at least to help bootstrapping local refinements based on partial differential equations [Whi98], [ZOF01], whose initialisation is often problematic.”
The space of human body shapes: reconstruction and parameterization from range scans (2003, methodology): “Their method and other recent, direct hole-free reconstruction methods [Carr et al. 2001; Whitaker 1998] have the nice feature that holes are filled in a smooth manner.”
A Framework for Geometric Analysis of Vascular Structures: Application to Cerebral Aneurysms (2009, methodology): “The evolution of is tracked using upwind finite differences in a narrow band of voxels around the 0-level set using the sparse field approach [21].”