Name
Abstract | ||
|---|---|---|
We present DeepMend, a novel approach to reconstruct resto- rations to fractured shapes using learned occupancy functions. Existing shape repair approaches predict low-resolution voxelized restorations or smooth restorations, or require symmetries or access to a pre-existing complete oracle. We represent the occupancy of a fractured shape as the conjunction of the occupancy of an underlying complete shape and a break surface, which we model as functions of latent codes using neural networks. Given occupancy samples from a fractured shape, we estimate latent codes using an inference loss augmented with novel penalties to avoid empty or voluminous restorations. We use the estimated codes to reconstruct a restoration shape. We show results with simulated fractures on synthetic and real-world scanned objects, and with scanned real fractured mugs. Compared to existing approaches and to two baseline methods, our work shows state-of-the-art results in accuracy and avoiding restoration artifacts over non-fracture regions of the fractured shape. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-20062-5_25 | Computer Vision – ECCV 2022 |
Keywords | DocType | ISSN |
Learned occupancy, Shape representation, Repair, Fracture, Implicit surface, Neural networks | Conference | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolas Lamb | 1 | 0 | 0.34 |
| Sean Banerjee | 2 | 0 | 0.34 |
| Natasha Kholgade Banerjee | 3 | 3 | 3.09 |