Our Innovations
Refining 3D Models with MeshLib’s Mesh Simplification
Precision Mesh Offsetting with MeshLib
3D Boolean Operations
Mesh Healing
Three main 3D data representations
Mesh algorithms
Collisions
Finds colliding faces of two meshes in realtime.
Distances
Finds minimal signed/unsigned distance between two meshes in realtime.
Free form deformation
Applies free form transformation to mesh or finds best free form transformation to fit given points. Grey points – reference grid of free form transform (resolution is customizable)
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Laplacian deformation
It modifies a surface region following the movement of some fixed points in this region, and preserving curvature in other points Read area – deformed area (a few points were fixed on forehead)
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Fill/Stitch Hole(s)
Triangulates a hole in mesh, or stitch two holes with a tunnel. Different metrics could be used for triangulation.
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Mesh Decimation
Decimation or simplification is reached by reducing the number of triangles, while preserving the overall initial shape and most prominent features.
Mesh before: ~200k triangles
Mesh after: 20k triangles
Shortest Geodesic (surface) path building
The task is to find the shortest path between two points along the surface. The implementation is very fast and robust allowing its application in real-time editing:
Iterative Closest Points
ICP is for finding the best aligning transformation (from some transformation group, e.g. the group of all rigid transformations) for two meshes.
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Tunnels fixing
MeshLib can detect and fix tunnels on surface. Here on the left a thin tunnel is detected, a cut is made, each side of which is filled with a disc-like patch to get a topological sphere on the right:
Topological torus
Topological sphere
3D Boolean Operations
MeshInspector contains two implementations of 3D Boolean operations:
- Voxel-based, that first converts closed meshes in 3D raster image (voxels), then makes the operations, and then converts it back. Any self-intersections are supported in input meshes, and are automatically fixed in output.
- Direct mesh-based operations , which can work even with not-closed meshes and supports various input degeneracies. The main advantage over voxel-based mode is higher performance and extreme precision not limited by the size of one voxel.
Sphere and Dino
Sphere – Dino (transparent)
Sphere – Dino form other side
Partial Offset
Offset selected part of mesh only and produce single component mesh as result.
Red faces are selected
Mesh with offset of selected parts
Segmentation by curvature
The task is to select a mesh region, separated from other mesh parts by a high curvature boundary (or some other metrics, including custom ones), given 2-3 points on the boundary.
The picture demonstrates segmentation of crests.
Each one is segmented by two points picked on its base
Compare with Precision
Effortlessly compare two models side by side, meticulously examining the surface distances. This innovative feature allows you to inspect the quality of your printed 3D model against the original CAD design. Identify even the slightest deviations and ensure your project meets the most stringent standards.
Distance map algorithms
Distance Map from mesh
Depth map from some plane to mesh. Could be visualized in grey-scale image:– Black pixels of distance map – farthest– White pixels – closest
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Distance Map from contours
It presents field of distances in contours plane from each pixel, to contours closest point.Can be signed or unsigned. Zero value corresponds to input contour points
Input contour
Signed distance map. Inner points have negative value white – the most “inner” point
Unsigned distance map. Inner points have positive value white – the most “inner” point
Distance Map iso-lines
Finds iso-line contour on distance map of any kind.
Mesh – distance map visualization
Red line – input contour
Blue lines – iso-lines
Distance Map merge
Merge min, max or subtraction of distance maps
Distance map A
Distance map B
Merge Maximum (intersection)
Merge Minimum (union)
Voxel-based algorithms
Voxels
Voxel object presents 3D grid in space (visualized via Iso surface). The Picture demonstrates dense voxels and its slice.
Semi-automatic segmentation
The picture demonstrates separation of tooth from the skull voxels.
CT-Reconstruction algorithm
CT-Reconstruction algorithm implementation allows you to obtain dense volumes (voxel data) from a number of radiograph images. Voxel data generated by CT-Reconstruction
Point cloud algorithms
Points Sampling
Sampling – algorithm for taking representative subset of points. Uniform and grid sampling are implemented.
All points
Sampled points
Points Triangulation
Creating mesh from point cloud
Other features
3D Viewer: Hierarchical object model for scene description
3D Viewer: History stack
3D Viewer: Feature search
3D Viewer: Customizable viewports
Visualization Features
Python scripting
