SARe: Structure-Aware Large-Scale 3D Fragment Reassembly

TL;DR

SARe is a novel structure-aware framework for large-scale 3D fragment reassembly that improves accuracy and stability by explicit contact modeling and a refinement stage, outperforming existing methods especially with many fragments.

Contribution

The paper introduces SARe, a generative and refinement framework with explicit contact modeling for large-scale 3D fragment reassembly, addressing failure issues of prior end-to-end approaches.

Findings

Achieves state-of-the-art performance on synthetic and real fractured scans.

Demonstrates higher success rates with increasing fragment count.

Provides more stable and consistent reassembly results.

Abstract

3D fragment reassembly aims to recover the rigid poses of unordered fragment point clouds or meshes in a common object coordinate system to reconstruct the complete shape. The problem becomes particularly challenging as the number of fragments grows, since the target shape is unknown and fragments provide weak semantic cues. Existing end-to-end approaches are prone to cascading failures due to unreliable contact reasoning, most notably inaccurate fragment adjacencies. To address this, we propose Structure-Aware Reassembly (SARe), a generative framework with SARe-Gen for Euclidean-space assembly generation and SARe-Refine for inference-time refinement, with explicit contact modeling. SARe-Gen jointly predicts fracture-surface token probabilities and an inter-fragment contact graph to localize contact regions and infer candidate adjacencies. It adopts a query-point-based conditioning scheme and extracts aligned local geometric tokens at query locations from a frozen geometry encoder, yielding queryable structural representations without additional structural pretraining. We further introduce an inference-time refinement stage, SARe-Refine. By verifying candidate contact edges with geometric-consistency checks, it selects reliable substructures and resamples the remaining uncertain regions while keeping verified parts fixed, leading to more stable and consistent assemblies in the many-fragment regime. We evaluate SARe across three settings, including synthetic fractures, simulated fractures from scanned real objects, and real physically fractured scans. The results demonstrate state-of-the-art performance, with more graceful degradation and higher success rates as the fragment count increases in challenging large-scale reassembly.