Depth Estimation using monocular images is an emerging field of research and hos shown interesting progress.
- NYUDepthV2: Nathan Silberman, Pushmeet Kohli, Derek Hoiem, Rob Fergus, Indoor Segmentation and Support Inference from RGBD Images, ECCV 2012, https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
- Models Link - NYUDepthV2
- Additional information
- Training Code
- Note: Depth estimation from monocular image input
| Dataset | Model Name | Input Size | GigaMACs | Delta1% | Available | Notes |
|---|---|---|---|---|---|---|
| NYUDepthV2 | Fast Depth | 224x224 | 0.3825 | 77.1 | Y |
- Models Link - NYUDepthV2
- Training Code
- Note: Depth estimation from monocular image input
| Dataset | Model Name | Input Size | GigaMACs | Delta1% | Available | Notes |
|---|---|---|---|---|---|---|
| NYUDepthV2 | MiDaS-small (v2.1) | 256x256 | 4.633 | 86.67 | Y |
[1] NYUDepthV2: Nathan Silberman, Pushmeet Kohli, Derek Hoiem, Rob Fergus, Indoor Segmentation and Support Inference from RGBD Images, ECCV 2012, https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html
[2] Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne, FastDepth: Fast Monocular Depth Estimation on Embedded Systems, IEEE International Conference on Robotics and Automation (ICRA), 2019
[3] Rene Ranftl and Katrin Lasinger and David Hafner and Konrad Schindler and Vladlen Koltun, Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020