mlsanityData to 7 Oct 2026

A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts

arXiv 2410.14770 ↗First posted 18 Oct 2024cs.CV cs.GRcited by 8

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Abstract (arXiv)

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. The 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 non-deep learning 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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 92 of its 321 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

92 of 321 references.

References · 92

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38 more
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50 most cited of 92 All 92 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025GARF: Learning Generalizable 3D Reassembly for Real-World Fracturescited by 12
  2. 2025Assembler: Scalable 3D Part Assembly via Anchor Point Diffusioncited by 11
  3. 2026TORA: Topological Representation Alignment for 3D Shape Assemblycited by 4
  4. 2025BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assemblycited by 3
  5. 2026The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragmentscited by 2
  6. 2026SARe: Structure-Aware Generative 3D Fragment Reassemblycited by 1
  7. 2026CRAG: Can 3D Generative Models Help 3D Assembly?cited by 0
  8. 2026An Invariant Tangent-Angle Descriptor and a Band U-Net for 2D Fragment Adjacency Predictioncited by 0