Name
Abstract | ||
|---|---|---|
Deep learning has become an increasingly popular approach for 3D reconstruction. While results look promising, deep learning produces black box networks which are hard to analyze. To apply such networks in the real world reliably, it is crucial that their functioning is well understood. In this paper we will evaluate to what extend current neural 3D reconstruction algorithms perform true 3D recons... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IVCNZ54163.2021.9653224 | 2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Keywords | DocType | ISSN |
Deep learning,Three-dimensional displays,Shape,Training data,Reliability,Image reconstruction | Conference | 2151-2191 |
ISBN | Citations | PageRank |
978-1-6654-0645-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bhargava Gowda | 1 | 0 | 0.34 |
| Burkhard C. Wünsche | 2 | 105 | 22.75 |
| Alex Shaw | 3 | 0 | 0.68 |