Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts. |
| Συγγραφείς: |
Lu, Jiaxin, Liang, Yongqing, Han, Huijun, Hua, Jiacheng, Jiang, Junfeng, Li, Xin, Huang, Qixing |
| Πηγή: |
Computer Graphics Forum; May2025, Vol. 44 Issue 2, p1-32, 32p |
| Θεματικοί όροι: |
Computer graphics, Algorithms, Deep learning, Image segmentation, Shape recognition (Computer vision) |
| Περίληψη: |
Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general‐purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open‐source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |