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In-depth: Depth Map Estimation From Monocular RGB Image Using Deep Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Singhal, Isha |
| Copyright Year | 2020 |
| Abstract | This report addresses the approaches of predicting depth from a single RGB image. Our implementation is based on the approaches presented by Eigen et al. (2) based on a two pass CNN model. This code is made available on Github (3). We evaluate the outcomes of our approaches in defined metrics and loss value. We also present several directions that we experimented in the project, such as data augmentation, batch normalization, dropout, and network architecture changes. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://cs230.stanford.edu/projects_winter_2020/posters/32072739.pdf |
| Alternate Webpage(s) | http://cs230.stanford.edu/projects_winter_2020/reports/32072739.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
