Name
Abstract | ||
|---|---|---|
Three-dimensional scene understanding is an emerging field in many real-world applications. Autonomous driving, robotics, and continuous real-time tracking are hot topics within the engineering society. One essential component of this is to develop faster and more reliable algorithms being capable of predicting depths from RGB images. Generally, it is easier to install a system with fewer cameras because it requires less calibration. Thus, our aim is to develop a strategy for predicting the depth on a single image as precisely as possible from one point of view. There are existing methods for this problem with promising results. The goal of this paper is to advance the state-of-the-art in the field of single-image depth prediction using convolutional neural networks. In order to do so, we modified an existing deep neural network to get improved results. The proposed architecture contains additional side-to-side connections between the encoding and decoding branches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-98678-4_38 | MULTIMEDIA AND NETWORK INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Depth estimation,Deep learning,CNN | Conference | 833 |
ISSN | Citations | PageRank |
2194-5357 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Károly Harsányi | 1 | 1 | 0.36 |
| Attila Kiss | 2 | 31 | 7.60 |
| András L. Majdik | 3 | 1 | 0.69 |
| Sziranyi, T. | 4 | 395 | 44.76 |