TY - GEN
T1 - MatchMesh
T2 - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
AU - Tang, Ying
AU - Sun, Shengtao
AU - Wu, Ben
N1 - Funding Information:
This work was supported in part by the National Science Foundation under Grant 1913809 and Grant 1610164.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process.
AB - The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process.
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U2 - 10.1109/ICNSC48988.2020.9238103
DO - 10.1109/ICNSC48988.2020.9238103
M3 - Conference contribution
AN - SCOPUS:85096354230
T3 - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
BT - 2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 October 2020 through 2 November 2020
ER -