Using t-distributed stochastic neighbor embedding for visualization and segmentation of 3D point clouds of plants

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DUTAĞACI H.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.31, no.5, pp.792-813, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 31 Issue: 5
  • Publication Date: 2023
  • Doi Number: 10.55730/1300-0632.4018
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.792-813
  • Keywords: Point clouds, plants, visualization, superpoint, segmentation, t-distributed stochastic neighbor embedding, PART SEGMENTATION, SNE
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

In this work, the use of t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. It is demonstrated that t-SNE operates as a practical tool to flatten and visualize a complete 3D plant model in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels. Aside from the promise of serving as a visualization tool for plant scientists, t-SNE also provides a gateway for processing 3D point clouds of plants using their embedded counterparts in 2D. In this paper, simple methods were proposed to perform semantic segmentation and instance segmentation via grouping the embedded 2D points. The evaluation of these methods on a public 3D plant data set conveys the potential of t-SNE for enabling 2D implementation of various steps involved in automatic 3D phenotyping pipelines.