news Mesh_3

_posts/2024-12-01-mesh3-improvements.md

@@ -19,40 +19,36 @@ Lately, CGAL authors have focused more on the quality of the output mesh, the ab
 mesh generation algorithm to represent the input surface in a more accurate way, for a wider range of input data types.
 </p>
 
-<!--
-<div style="text-align:center;">
-  <a href="../../../../images/christmas_wrap.png"><img src="../../../../images/christmas_wrap.png" style="max-width:95%"/></a><br>
-  <br><small>Season's wrappings.</small>
-</div>
--->
-
 <br>
 <p>Recent releases of CGAL have introduced improvements to the 3D Tetrahedral Mesh Generation package:
 detection and protection of triple lines from labeled images,
 generation and use of weighted labeled images,
-a new approximation error criterion for polyline features,
-and the ability to deal with self-intersecting input surfaces.</p>
+and a new approximation error criterion for polyline features.</p>
+
 
 <br>
 <h3>Triple lines from labeled images</h3>
 
 <p>CGAL's tetrahedral mesh generation algorithm is able to compute tetrahedral meshes from 3D segmented images.
 For all types of inputs, detection and construction of one-dimensional features must be done a priori for the
 feature protection algorithm to be able to represent them properly in the generated mesh.
-In 3D segmented images, such features correspond to the intersections of the bounding box of the image with the surfaces defined by the image, as well as polylines that lie at the intersection of three or more subdomains (including the outside).</p>
+In 3D segmented images, such features correspond to the intersections of the bounding box of the image with the surfaces
+defined by the image, as well as polylines that lie at the intersection of three or more subdomains (including the outside).</p>
 
 <p>The article
-<a href="https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/274"><i>Hege, H. C., Seebass, M., Stalling, D., & Zöckler, M. (1997), A generalized marching cubes algorithm based on non-binary classifications</i></a>, provides a list of possible voxel configurations. This description has been generalized for tetrahedral mesh generation needs, and
+<a href="https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/274"><i>Hege, H. C., Seebass, M., Stalling, D., & Zöckler, M. (1997),
+A generalized marching cubes algorithm based on non-binary classifications</i></a>, provides a list of possible voxel configurations.
+This description has been generalized for tetrahedral mesh generation needs, and
 implemented in CGAL.</p>
 
 <p>After feature lines detection, similarly to what is done for polyhedral surfaces, the polyline features
 are added to the feature graph of the domain for feature protection.</p>
 
 <p>Two detection functors are provided :
 <ul>
-  <li><i>CGAL::Detect_features_in_image()</i>, that detects and constructs all
+  <li><i>CGAL::Detect_features_in_image()</i> detects and constructs all
     polylines lying at the intersection of three or more subdomains, and</li>
-  <li><i>CGAL::Detect_features_on_image_bbox()</i>, that detects and constructs the polylines
+  <li><i>CGAL::Detect_features_on_image_bbox()</i> detects and constructs the polylines
     that lie at the intersection of two or more subdomains and the bounding box of the input labeled image.</li>
 </ul>
 </p>
@@ -72,9 +68,10 @@ are added to the feature graph of the domain for feature protection.</p>
 <p>When a segmented image is given as input for tetrahedral mesh generation,
 the generated mesh surface sometimes sticks too closely to the voxels surface, causing an aliasing effect. A solution to generate
 a smooth and accurate output surface was described by Stalling et al in
-<a href="https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/382"><i>Stalling, D., Zöckler, M., Sander, O., & Hege, H. C. (1998), Weighted labels for 3D image segmentation</i></a>.
+<a href="https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/382"><i>Stalling, D., Zöckler, M., Sander, O., & Hege, H. C. (1998),
+Weighted labels for 3D image segmentation</i></a>.
 It consists in generating a second input image, made of integer coefficients called weights, and use those weights to define
-smoother domain boundaries. The 3D image of weights can be generated using <i>CGAL::Mesh_3::generate_label_weights()</i>.</p>
+smoother domain boundaries. The 3D image of weights can be generated by <i>CGAL::Mesh_3::generate_label_weights()</i>.</p>
 
 <div style="text-align:center;">
   <a href="../../../../images/mesh3_weighted_images.png"><img src="../../../../images/mesh3_weighted_images.png" style="max-width:95%"/></a><br>
@@ -83,7 +80,7 @@ smoother domain boundaries. The 3D image of weights can be generated using <i>CG
 </div>
 
 <br>
-<p>The following example shows how to use weighted labeled images: <a href="https://doc.cgal.org/latest/Mesh_3/Mesh_3_2mesh_3D_weighted_image_8cpp-example.html">mesh_3D_weighted_image.cpp</a>.</p>
+<p>The following example shows how to use weighted labeled images: <a href="https://doc.cgal.org/6.0.1/Mesh_3/Mesh_3_2mesh_3D_weighted_image_8cpp-example.html">mesh_3D_weighted_image.cpp</a>.</p>
 
 <p>Utilizing tetrahedral mesh generation from labeled images, incorporating both feature detection and weighted labels,
 presents challenges. A new method that combines feature detection and weighted labels has been
@@ -100,22 +97,22 @@ The list of CGAL meshing criteria has been enriched with a new criterion that en
 define an upper bound on the distance between the input feature graph and the output feature edges.</p>
 
 <p>The following example shows how to use this new criterion, called <i>edge_distance</i>:
-<a href="https://doc.cgal.org/6.0-beta1/Mesh_3/Mesh_3_2mesh_polyhedral_domain_with_edge_distance_8cpp-example.html">
+<a href="https://doc.cgal.org/6.0.1/Mesh_3/Mesh_3_2mesh_polyhedral_domain_with_edge_distance_8cpp-example.html">
 mesh_polyhedral_domain_with_edge_distance.cpp</a>.</p>
 
-#TODO : add image here to illustrate edge_distance
-
-<br>
-<h3>Self-intersecting input surfaces</h3>
-
-#TODO : write another news entry on that topic
+<div style="text-align:center;">
+  <a href="../../../../images/mesh3_edge_distance_stool.png"><img src="../../../../images/mesh3_edge_distance_stool.png" style="max-width:75%"/></a><br>
+  <br><small>Mesh generation from polyhedral surface with varying <i>edge_distance</i> meshing criterion.
+  The quality improvement is important on the lower end of stool feet.</small>
+</div>
 
 <h3>Status</h3>
 
-<p>All these new improvements are already integrated in CGAL's master branch on the
-<a href="https://github.com/CGAL/cgal/">CGAL GitHub repository</a>, and has just been officially released
-in the latest version of CGAL, CGAL 6.0-beta1.</p>
+<p>All these new improvements have been integrated in CGAL-6.0.1, which has just been officially released.</p>
 
+<i class="bi bi-book"></i>
+<a href="https://www.cgal.org/2024/10/22/cgal601/">CGAL-6.0.1 announcement</a>
+<br>
 <i class="bi bi-book"></i>
 <a href="https://doc.cgal.org/latest/Manual/packages.html#PkgMesh3">Documentation of the package Mesh_3</a>
 <br>