# GeoAssemble: A Geometry-Aware Hierarchical Method for Point Cloud-Based Multi-Fragment Assembly

## TL;DR

GeoAssemble is a new method for assembling fragmented 3D objects by improving feature representation and matching accuracy.

## Contribution

GeoAssemble introduces a geometry-aware hierarchical framework with enhanced feature extraction and matching strategies.

## Key findings

- GeoAssemble outperforms baseline methods in matching accuracy on both synthetic and real-world datasets.
- The two-stage matching strategy reduces ambiguity and improves robustness in multi-fragment assembly.
- The proposed transformation estimation mechanism enhances alignment accuracy and convergence stability.

## Abstract

Three-dimensional fragment assembly technology has significant application value in fields such as cultural relic restoration, medical image analysis, and industrial quality inspection. To address the common challenges of limited feature representation ability and insufficient assembling accuracy in existing methods, this paper proposes a geometry-aware hierarchical fragment assembly framework (GeoAssemble). The core contributions of our work are threefold: first, the framework utilizes DGCNN to extract local geometric features while integrating centroid relative positions to construct a multi-dimensional feature representation, thereby enhancing the identification quality of fracture points; secondly, it designs a two-stage matching strategy that combines global shape similarity coarse matching with local geometric affinity fine matching to effectively reduce matching ambiguity; finally, we propose an auxiliary transformation estimation mechanism based on the geometric center of fracture point clouds to robustly initialize pose parameters, thereby improving both alignment accuracy and convergence stability. Experiments conducted on both synthetic and real-world fragment datasets demonstrate that this method significantly outperforms baseline methods in matching accuracy and exhibits higher robustness in multi-fragment scenarios.