Efficient conversion of point cloud model into finite element accurate mesh model

[zhanglepy]

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Required Info
Camera Model	{ R200 / F200 / SR300 / ZR300 / D400 }
Firmware Version	(Open RealSense Viewer --> Click info)
Operating System & Version	{Win (8.1/10) / Linux (Ubuntu 14/16/17) / MacOS
Kernel Version (Linux Only)	(e.g. 4.14.13)
Platform	PC/Raspberry Pi/ NVIDIA Jetson / etc..
SDK Version	{ legacy / 2.. }
Language	{C/C#/labview/nodejs/opencv/pcl/python/unity }
Segment	{Robot/Smartphone/VR/AR/others }

Issue Description

<Describe your issue / question / feature request / etc..>
Hello,
By using a depth camera to obtain the surface point cloud model of an object (in ply format), and then stitching it together from multiple perspectives to form a complete point cloud model, what processing is needed to efficiently convert it into a finite element accurate mesh model suitable for SOFA deformation simulation. I previously tried using meshlab software to convert point clouds into models, then using FreeCAD to convert the models into solids, and finally using Gmsh to partition 2D and 3D meshes. However, the grid model established in this way is relatively rough, with a low success rate and a complex process.
I look forward to your help!

[MartyG-RealSense]

Hi @zhanglepy If you are stitching together .ply files then it may be worth next trying the free open-source CloudCompare tool - https://www.danielgm.net/cc/ - for merging them, as advised at #12611 (comment)

If you only need to convert the point cloud to mesh then CloudCompare has support for poisson reconstruction to perform the conversion.

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Poisson reconstruction can also be done in MeshLab, as described in the section of the RealSense Viewer to MeshLab .ply conversion guide headed Simplification / Sampling

https://www.andreasjakl.com/capturing-3d-point-cloud-intel-realsense-converting-mesh-meshlab/

[zhanglepy]

Thanks for your reply！

[MartyG-RealSense]

Hi @zhanglepy Do you require further assistance with this case, please? Thanks!