Noise analysis and synthesis for 3D laser depth scanners
Graphical Models
Evaluation for small visual difference between conforming meshes on strain field
Journal of Computer Science and Technology
Comparing small visual differences between conforming meshes
GMP'08 Proceedings of the 5th international conference on Advances in geometric modeling and processing
Perception of linear and nonlinear motion properties using a FACS validated 3D facial model
Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization
Feature-preserving mesh denoising based on vertices classification
Computer Aided Geometric Design
Short Communication to SMI 2011: Surface feature based mesh segmentation
Computers and Graphics
Prominent Field for Shape Processing and Analysis of Archaeological Artifacts
International Journal of Computer Vision
A cascaded approach for feature-preserving surface mesh denoising
Computer-Aided Design
Surface mesh denoising with normal tensor framework
Graphical Models
A framework for 3D model reconstruction in reverse engineering
Computers and Industrial Engineering
Edge-aware point set resampling
ACM Transactions on Graphics (TOG)
A robust algorithm for denoising meshes with high-resolution details
CVM'12 Proceedings of the First international conference on Computational Visual Media
Polygon mesh repairing: An application perspective
ACM Computing Surveys (CSUR)
Finite element mesh generation for composites with ply waviness based on X-ray computed tomography
Advances in Engineering Software
Mesh denoising via L0 minimization
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
CAD/Graphics 2013: Feature-preserving filtering with L0 gradient minimization
Computers and Graphics
Consolidation of low-quality point clouds from outdoor scenes
SGP '13 Proceedings of the Eleventh Eurographics/ACMSIGGRAPH Symposium on Geometry Processing
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We present a simple and fast mesh denoising method, which can remove noise effectively while preserving mesh features such as sharp edges and corners. The method consists of two stages. First, noisy face normals are filtered iteratively by weighted averaging of neighboring face normals. Second, vertex positions are iteratively updated to agree with the denoised face normals. The weight function used during normal filtering is much simpler than that used in previous similar approaches, being simply a trimmed quadratic. This makes the algorithm both fast and simple to implement. Vertex position updating is based on the integration of surface normals using a least-squares error criterion. Like previous algorithms, we solve the least-squares problem by gradient descent; whereas previous methods needed user input to determine the iteration step size, we determine it automatically. In addition, we prove the convergence of the vertex position updating approach. Analysis and experiments show the advantages of our proposed method over various earlier surface denoising methods.