3D Shape Knowledge Graph for Cross-domain 3D Shape Retrieval
Rihao Chang, Yongtao Ma, Tong Hao, Weizhi Nie

TL;DR
This paper introduces a novel 3D shape knowledge graph using geometric words as nodes, enabling effective cross-domain and cross-modal 3D shape retrieval with superior performance on multiple datasets.
Contribution
The study proposes a new geometric word-based knowledge graph and a graph embedding method to improve cross-domain and cross-modal 3D shape retrieval.
Findings
Outperforms state-of-the-art methods on ModelNet40 and ShapeNetCore55 datasets.
Effective in cross-domain 3D shape retrieval tasks.
Demonstrates superiority in cross-modal retrieval on MI3DOR dataset.
Abstract
The surge in 3D modeling has led to a pronounced research emphasis on the field of 3D shape retrieval. Numerous contemporary approaches have been put forth to tackle this intricate challenge. Nevertheless, effectively addressing the intricacies of cross-modal 3D shape retrieval remains a formidable undertaking, owing to inherent modality-based disparities. This study presents an innovative notion, termed "geometric words", which functions as elemental constituents for representing entities through combinations. To establish the knowledge graph, we employ geometric words as nodes, connecting them via shape categories and geometry attributes. Subsequently, we devise a unique graph embedding method for knowledge acquisition. Finally, an effective similarity measure is introduced for retrieval purposes. Importantly, each 3D or 2D entity can anchor its geometric terms within the knowledge…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topics3D Shape Modeling and Analysis · Image Retrieval and Classification Techniques · 3D Surveying and Cultural Heritage
