Feature Preserving Mesh Simplification Research Articles

A Cluster-Based 3D Reconstruction System for Large-Scale Scenes

The reconstruction of realistic large-scale 3D scene models using aerial images or videos has significant applications in smart cities, surveying and mapping, the military and other fields. In the current state-of-the-art 3D-reconstruction pipeline, the massive scale of the scene and the enormous amount of input data are still considerable obstacles to the rapid reconstruction of large-scale 3D scene models. In this paper, we develop a professional system for large-scale 3D reconstruction. First, in the sparse point-cloud reconstruction stage, the computed matching relationships are used as the initial camera graph and divided into multiple subgraphs by a clustering algorithm. Multiple computational nodes execute the local structure-from-motion (SFM) technique, and local cameras are registered. Global camera alignment is achieved by integrating and optimizing all local camera poses. Second, in the dense point-cloud reconstruction stage, the adjacency information is decoupled from the pixel level by red-and-black checkerboard grid sampling. The optimal depth value is obtained using normalized cross-correlation (NCC). Additionally, during the mesh-reconstruction stage, feature-preserving mesh simplification, Laplace mesh-smoothing and mesh-detail-recovery methods are used to improve the quality of the mesh model. Finally, the above algorithms are integrated into our large-scale 3D-reconstruction system. Experiments show that the system can effectively improve the reconstruction speed of large-scale 3D scenes.

Feature-preserving mesh simplification through anisotropic Nyquist-based adaptive sampling of points inside the segmented regions

Feature-preserving mesh simplification through anisotropic Nyquist-based adaptive sampling of points inside the segmented regions

三角网格模型的特征保持混合折叠简化

三角网格模型的特征保持混合折叠简化

Interest Points Guided Mesh Simplification

Based on interest point detection, a feature preserving mesh simplification algorithm is proposed. The Harris operator values of all vertices in the mesh were computed firstly. On the base of Garland’s simplification algorithm, we combine the Harris operator value with quadric error metric and change the order of edge collapsing in the simplification. The experimental results show that the proposed algorithm is effective and feature preserving.

Feature Preserving Mesh Simplification Using Feature Sensitive Metric

We present a new method for feature preserving mesh simplification based on feature sensitive (FS) metric. Previous quadric error based approach is extended to a high-dimensional FS space so as to measure the geometric distance together with normal deviation. As the normal direction of a surface point is uniquely determined by the position in Euclidian space, we employ a two-step linear optimization scheme to efficiently derive the constrained optimal target point. We demonstrate that our algorithm can preserve features more precisely under the global geometric properties, and can naturally retain more triangular patches on the feature regions without special feature detection procedure during the simplification process. Taking the advantage of the blow-up phenomenon in FS space, we design an error weight that can produce more suitable results. We also show that Hausdorff distance is markedly reduced during FS simplification

Feature Preserving Mesh Simplification Algorithm Based on Square Volume Measure

A new algorithm for mesh simplification with triangle collapse based on square volume measure is presented in this paper.Models can be simplified by minimizing error objective function.Square volume error,shape factor and normal constraint factor of triangles are combined together to define the simplification error,which can be described as a quadratic objective function.New vertices replaced collapsed triangles can be determined linearly.Comparing with the QEM method which is the most effective method so far,computation complexity will not be increased.Gaussian curvature factor is computed for each collapsed triangle and used to identify strong feature triangle.For non-strong feature triangle,new vertex position is determined by minimizing the error objective function.For strong feature triangle,new vertex is taken the strong feature vertex of the three triangle vertices for preserving the model feature.Each collapsed triangle is processed as inner triangle or boundary triangle to keep the boundary feature.Experiments for the efficiency of the new algorithm are included.

Feature Preserving Mesh Simplification Based on Corner Cutting

Feature Preserving Mesh Simplification Based on Corner Cutting