SARe: Structure-Aware Generative 3D Fragment Reassembly
Authors: Hanze Jia, Chunshi Wang, Yuxiao Yang, Zhonghua Jiang, Yawei Luo, Shuainan Ye, Tan Tang
Organizations: State Key Lab of CAD&CG, Zhejiang University · School of Software Technology, Zhejiang University · Laboratory of Art and Archaeology Image, Zhejiang University
3D fragment reassembly estimates the rigid pose of each fragment to recover a complete object from unordered point clouds or meshes. The task becomes increasingly challenging as the fragment count grows, since irregular fragments provide weak semantic cues and admit rapidly increasing numbers of plausible contact relations and global configurations. We propose Structure-Aware Reassembly (SARe), a generative framework that integrates query-aligned local geometry and task-native structural supervision into point-flow assembly. SARe-Gen conditions each transported surface query on a local latent and jointly supervises intermediate flow tokens with query-level fracture-region and fragment-level contact targets. Because these heads are optimized together with flow matching, structural supervision directly shapes the representations that drive coordinate transport, without requiring additional reassembly-specific pretraining or a separate teacher-alignment stage. At inference time, SARe-Refine geometrically verifies predicted relations and uses reliable local subassemblies to guide a second sampling pass, reinforcing consistent regions while resampling uncertain fragments. We evaluate SARe across three settings, including synthetic fractures, simulated fractures from scanned real objects, and scans of physically fractured objects. The results demonstrate state-of-the-art performance, with higher part accuracy and more graceful degradation in challenging many-fragment settings.
Figures & tables
Figure 1: Representative oracle partial-structure diagnostic on RPF. Preserving a local subassembly specified by ground-truth contact edges while regenerating the remaining fragments improves PA from 75% to 82% in this example.
Figure 2: Overview of SARe. SARe-Gen transports query-aligned points into the assembled frame while jointly predicting query-level fracture-region probabilities and a fragment-level contact graph. SARe-Refine geometrically verifies the predicted relations and uses the retained local subassemblies to guide second-pass resampling.
Figure 3: SARe-Refine pipeline. Predicted contact edges are filtered to identify reliable substructures, after which uncertain regions are resampled in a RePaint-style process conditioned on a stable mask.
Everyday, K=2 – 20
Everyday, K=21 – 50
Artifact, K=2 – 20
Artifact, K=21 – 50
Method
PA ↑ (%)
CD ↓×10−3
RE ↓ (deg.)
TE ↓×10−2
PA ↑ (%)
CD ↓×10−3
RE ↓ (deg.)
TE ↓×10−2
PA ↑ (%)
CD ↓×10−3
RE ↓ (deg.)
TE ↓×10−2
PA ↑ (%)
CD ↓×10−3
RE ↓ (deg.)
TE ↓×10−2
Jigsaw
57.30
13.30
42.30
10.70
–
–
–
–
45.60
14.30
52.40
22.20
–
–
–
–
PF++
72.40
3.65
39.20
8.46
–
–
–
–
53.53
7.63
51.26
14.37
–
–
–
–
GARF
93.22
1.10
20.01
4.17
51.80
5.99
75.52
31.97
89.22
2.79
29.87
4.09
37.23
6.30
68.84
29.41
RPF
93.39
0.78
20.60
3.90
58.51
4.70
75.18
21.56
92.76
1.60
25.60
4.06
57.16
5.92
75.64
22.28
TORA-CKA
94.92
0.58
15.42
3.84
71.74
1.58
65.33
15.23
94.13
0.86
21.45
5.46
68.39
3.36
64.73
15.82
Table 1: Quantitative comparison on the non-volume-constrained Breaking Bad benchmark. Results are reported on the Everyday and Artifact subsets under different fragment-count ranges.
Method
PA ↑
CD ↓
RE ↓
TE ↓
GARF
80.12
1.32
22.54
5.37
RPF
82.32
1.35
20.06
4.33
TORA-CKA
93.75
0.47
14.78
1.94
SARe-Gen
97.68
0.40
9.92
1.06
Table 2: Quantitative results on scan-derived simulated fractures. OmniObject3D-Fracture is generated by applying simulated fracture processes to real-world scanned objects.
FRACTURA
Fantastic Breaks
Method
PA ↑
CD ↓
RE ↓
TE ↓
PA ↑
CD ↓
RE ↓
TE ↓
GARF
44.26
6.01
41.07
26.91
88.31
2.12
8.20
2.10
RPF
68.11
3.64
51.15
11.23
96.90
2.53
6.32
2.18
TORA-CKA
76.35
1.89
36.40
7.72
97.28
0.26
3.03
0.80
SARe-Gen
81.44
0.59
19.23
4.14
98.53
0.23
7.78
0.35
Table 3: Zero-shot transfer to unseen fracture datasets. All methods are evaluated without fine-tuning.
Method
PA ↑
CD ↓
RE ↓
TE ↓
SARe-Gen
81.34
1.85
34.35
6.83
SARe-Refine
84.89
1.72
32.51
6.19
Δ
+3.55
-0.13
-1.84
-0.64
Table 4: Effect of SARe-Refine on difficult Breaking Bad cases. Results are averaged over the N=2411 pooled Breaking Bad test samples whose first-pass PA is below 95%.
Figure 4: Qualitative comparisons of SARe-Gen and SARe-Refine. Red circles highlight regions corrected by the second pass.
Figure 5: Ablations of model components and head attachment layers, with a fracture-localization probe.
Setting
Time/obj. (s)
PA all ↑ (%)
PA K=21–50↑ (%)
M=2560
0.79
93.06
69.35
M=5120
1.03
94.81
83.05
M=10240
3.13
95.19
84.89
Uniform ( M=5120 )
1.02
79.69
44.96
Table 5: Test-time query-budget scaling and query-allocation ablation using the same checkpoint trained with M=5120 .