Shape similarity measurement for 3D mechanical part using D2 shape distribution and negative feature decomposition

Han Chung Cheng, Cheng Hung Lo, Chih Hsing Chu*, Yong Se Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

This paper proposes a novel measurement scheme of 3D shape similarity that integrates D2 Shape Descriptor and Negative Feature Decomposition (NFD). Using NFD, the scheme firstly converts a 3D mechanical part into a tree structure of geometrical primitives decomposed from the part model, namely Negative Feature Tree (NFT). The D2 shape descriptions of these primitives are then produced for further similarity assessments. We assess the shape similarity on a level-by-level basis between the NFTs of a query part and a candidate part. The weighted sum of the similarity values computed on each level is then used as a measure of the overall similarity between the two parts. Our approach combines the simplicity of D2 shape description while overcoming its insensitivity to negative features with NFD. It performs more consistently than the method of Convex Hull Difference (CHD). A comparison with the assessment results using D2 and CHD demonstrates the effectiveness of the new scheme.

Original languageEnglish
Pages (from-to)269-280
Number of pages12
JournalComputers in Industry
Volume62
Issue number3
DOIs
Publication statusPublished - Apr 2011
Externally publishedYes

Keywords

  • 3D shape similarity
  • D2
  • Mechanical part
  • Negative feature decomposition
  • Part search

Fingerprint

Dive into the research topics of 'Shape similarity measurement for 3D mechanical part using D2 shape distribution and negative feature decomposition'. Together they form a unique fingerprint.

Cite this