Feature detection for surface meshes (original) (raw)
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Proceedings. International Conference on Image Processing, 2002
Triangle meshes are widely used in representing surfaces in computer vision and computer graphics. Although 2D image processingbased edge detection techniques have been popular in many application areas, they are not well developed for surfaces represented by triangle meshes. This paper proposes a robust edge detection algorithm for triangle meshes and its applications to surface segmentation and adaptive surface smoothing. The proposed edge detection technique is based on eigen analysis of the surface normal vector field in a geodesic window. To compute the edge strength of a certain vertex, the neighboring vertices in a specified geodesic distance are involved. Edge information are used further to segment the surfaces with watershed algorithm and to achieve edgepreserved, adaptive surface smoothing. The proposed algorithm is novel in robustly detecting edges on triangle meshes against noise. The 3D watershed algorithm is an extension from previous work. Experimental results on surfaces reconstructed from multi-view real range images are presented.
MeshLab: an Open-Source Mesh Processing Tool
2008
The paper presents MeshLab, an open source, extensible, mesh processing system that has been developed at the Visual Computing Lab of the ISTI-CNR with the helps of tens of students. We will describe the MeshLab architecture, its main features and design objectives discussing what strategies have been used to support its development. Various examples of the practical uses of MeshLab in research and professional frameworks are reported to show the various capabilities of the presented system.
Feature detection of triangular meshes via neighbor supporting
2012
We propose a robust method for detecting features on triangular meshes by combining normal tensor voting with neighbor supporting. Our method contains two stages: feature detection and feature refinement. First, the normal tensor voting method is modified to detect the initial features, which may include some pseudo features. Then, at the feature refinement stage, a novel salient measure deriving from the idea of neighbor supporting is developed. Benefiting from the integrated reliable salient measure feature, pseudo features can be effectively discriminated from the initially detected features and removed. Compared to previous methods based on the differential geometric property, the main advantage of our method is that it can detect both sharp and weak features. Numerical experiments show that our algorithm is robust, effective, and can produce more accurate results. We also discuss how detected features are incorporated into applications, such as feature-preserving mesh denoising and hole-filling, and present visually appealing results by integrating feature information.
Feature sensitive mesh segmentation
Proceedings of the 2006 ACM …, 2006
Segmenting meshes into natural regions is useful for model understanding and many practical applications. In this paper, we present a novel, automatic algorithm for segmenting meshes into meaningful pieces. Our approach is a clustering-based top-down hierarchical segmentation algorithm. We extend recent work on feature sensitive isotropic remeshing to generate a mesh hierarchy especially suitable for segmentation of large models with regions at multiple scales. Using integral invariants for estimation of local characteristics, our method is robust and efficient. Moreover, statistical quantities can be incorporated, allowing our approach to segment regions with different geometric characteristics or textures. *
Mesh Segmentation - A Comparative Study
Shape Modeling International, 2006
Mesh segmentation has become an important compo- nent in many applications in computer graphics. In the last several years, many algorithms have been proposed in this growing area, offering a diversity of methods and vari- ous evaluation criteria. This paper provides a comparative study of some of the latest algorithms and results, along sev- eral axes. We evaluate only algorithms
Surface mesh segmentation and smooth surface extraction through region growing
Computer Aided Geometric Design, 2005
Laser range-scanners are used in fields as diverse as product design, reverse engineering, and rapid prototyping to quickly acquire geometric surface data of parts and models. This data is often in the form of a dense, noisy surface mesh that must be simplified into piecewise-smooth surfaces. The method presented here facilitates this time-consuming task by automatically segmenting a dense mesh into regions closely approximated by single surfaces. The algorithm first estimates the noise and curvature of each vertex. Then it filters the curvatures and partitions the mesh into regions with fundamentally different shape characteristics. These regions are then contracted to create seed regions for region growing. For each seed region, the algorithm iterates between region growing and surface fitting to maximize the number of connected vertices approximated by a single underlying surface. The algorithm finishes by filling segment holes caused by outlier noise. We demonstrate the algorithm effectiveness on real data sets.
Principal Curvature-Driven Segmentation of Mesh Models: A Preliminary Assessment
2021
Three methods for triangle mesh segmentation, based on precomputed principal curvature values and using a region growing algorithm to label the vertices defining distinct surface regions, were developed, aiming at supporting the /ater manipulation of mesh models. Examples are presented, using different models, to illustrate their behavior. Results are promising but, in some cases, there is a clear need for a further post-processing step to refine the boundaries between adjoining regions and eliminate segmentation artifacts.
2011
In recent years, we have witnessed a striking increase in research concerning how to describe a meshed surface. These descriptors are commonly used to encode mesh properties or guide mesh processing, not to augment existing computations by replication. In this work, we first define a robust surface descriptor based on a local height field representation, and present a transformation via the extraction of Zernike moments. Unlike previous work, our local surface descriptor is innately rotationally invariant. Second, equipped with this novel descriptor, we present SAMPLE -similarity augmented mesh processing using local exemplars -a method which uses feature neighbourhoods to propagate mesh processing done in one part of the mesh, the local exemplar, to many others. Finally, we show that SAMPLE can be used in a number of applications, such as detail transfer and parameterization.