MeshLab: an Open-Source Mesh Processing Tool

By Paolo Cignoni, M. Callieri, Massimiliano Corsini, M. Dellepiane, F. Ganovelli, G. Ranzuglia. 2008

2,846 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Management Science and Operations Research

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Citations in the corpus

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images (2018, methodology): “The mesh model is generated by implicit surface algo-rithm in Meshlab [7] and contains 156 vertices.”
Neighbors Do Help: Deeply Exploiting Local Structures of Point Clouds (2017, methodology): “To obtain 3D point clouds, we uniformly sample points from meshes by Poisson disk sampling using MeshLab [3] and normalize them into a unit ball.”
Shrec'16 Track: Retrieval of Human Subjects from Depth Sensor Data (2016, methodology): “…LEFT ELBOW, LEFT HAND, RIGHT SHOULDER, RIGHT EL-BOW, RIGHT HAND, TORSO, LEFT HIP, LEFT KNEE, LEFT FOOT, RIGHT HIP, RIGHT KNEE, RIGHT FOOT) and connectivity in .off format, and a triangulated mesh obtained with Meshlab [CCC ∗ 08] implementation of the ball pivoting algorithm [BMR ∗ 99] (Figure 4).”
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation (2023, methodology): “After extracting the mesh using Marching Cubes (Lorensen & Cline, 1998), we apply isotropic remeshing and quadric edge collapse decimation (Cignoni et al., 2008) to control the mesh complexity.”
ImageJ2: ImageJ for the next generation of scientific image data (2017, methodology): “marchingCubes op, an implementation of the marching cubes algorithm [95], visualized using MeshLab [96].”
How to build a dinosaur: Musculoskeletal modeling and simulation of locomotor biomechanics in extinct animals (2020, methodology): “2B); this may in turn require that the bone model be “trimmed” to remove all non-articular surface geometry beforehand, which can be accomplished in many software packages, including opensource packages such as MeshLab (Cignoni et al. 2008) and Blender (Blender Community 2018).”
PU-Net: Point Cloud Upsampling Network (2018, result): “Therefore, we compare the reconstruction result of our method with those baseline methods with direct Poisson surface reconstruction approach [13] implemented in MeshLab [6].”
Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling (2017, positive): “As in PointNet, to obtain 3D point clouds, we uniformly sampled points from meshes into 1024 points of each object by Poisson disk sampling using MeshLab [6] and normalized them into a unit ball.”
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans (2021, methodology): “Docker containing the annotator and properly linked software (Blender [16], compatible Python versions, Mesh-Lab [15], etc.).”
2D and 3D Roughness Characterization (2019, methodology): “It is recommended to use a third-party STL viewer such as GOM Inspect (GOM 2018) or MeshLab (Cignoni et al. 2008) prior to processing the data to ensure data quality.”
Physically Realizable Adversarial Examples for LiDAR Object Detection (2020, methodology): “We apply uniform mesh re-sampling in meshlab [11] to reduce the number of faces and produce regular geometry prior to deformation.”
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation (2019, methodology): “In our experiment, we use Meshlab [6] to generate level-1 icosahedron [47] vertices coordinates and corresponding edges for deformation hypotheses and local GCN graph topology.”
StarMap for Category-Agnostic Keypoint and Viewpoint Estimation (2018, methodology): “Also, annotating 3D keypoints of a CAD model [47] is not a hard task, given an interactive 3D UI such as MeshLab [5].”