Name
Abstract | ||
|---|---|---|
This paper tackles a challenging problem of monocular depth estimation aiming for partially-known environments. We propose a novel deep convolutional neural network architecture which takes an RGB image and partial depth samples to estimate an accurate full depth map of a scene. The network is equipped with a newly proposed dense depth sampling strategy and input skip connection that drastically improve estimation performance. We also introduce a novel combined loss function to encourage spatial smoothness of predicted depth maps. The evaluation result shows that our architecture achieves significant performance improvements over the baseline method on a newly created depth estimation dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GCCE46687.2019.9015566 | GCCE |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naoshi Kaneko | 1 | 0 | 1.69 |
| Yoshiaki Akazawa | 2 | 0 | 0.34 |
| Kazuhiko Sumi | 3 | 192 | 24.84 |