CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
- URL: http://arxiv.org/abs/2207.01218v1
- Date: Mon, 4 Jul 2022 06:06:46 GMT
- Title: CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
- Authors: Jiahui Wang, Haiyue Zhu, Haoren Guo, Abdullah Al Mamun, Vadakkepat
Prahlad, Tong Heng Lee
- Abstract summary: We develop a few-shot learning-based approach for effective part segmentation in CAM/CAD.
As a result, it not only reduces the requirements for the usually unattainable and exhaustive completeness of supervision datasets, but also improves the flexibility for real-world applications.
- Score: 3.590084255075439
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 3D part segmentation is an essential step in advanced CAM/CAD workflow.
Precise 3D segmentation contributes to lower defective rate of work-pieces
produced by the manufacturing equipment (such as computer controlled CNCs),
thereby improving work efficiency and attaining the attendant economic
benefits. A large class of existing works on 3D model segmentation are mostly
based on fully-supervised learning, which trains the AI models with large,
annotated datasets. However, the disadvantage is that the resulting models from
the fully-supervised learning methodology are highly reliant on the
completeness of the available dataset, and its generalization ability is
relatively poor to new unknown segmentation types (i.e. further additional
novel classes). In this work, we propose and develop a noteworthy few-shot
learning-based approach for effective part segmentation in CAM/CAD; and this is
designed to significantly enhance its generalization ability and flexibly adapt
to new segmentation tasks by using only relatively rather few samples. As a
result, it not only reduces the requirements for the usually unattainable and
exhaustive completeness of supervision datasets, but also improves the
flexibility for real-world applications. As further improvement and innovation,
we additionally adopt the transform net and the center loss block in the
network. These characteristics serve to improve the comprehension for 3D
features of the various possible instances of the whole work-piece and ensure
the close distribution of the same class in feature space. Moreover, our
approach stores data in the point cloud format that reduces space consumption,
and which also makes the various procedures involved have significantly easier
read and edit access (thus improving efficiency and effectiveness and lowering
costs).
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