| ### Genral-purpose models | |
| The general-purpose models are affine-invariant and as such need a pre-alignment step before an error can be computed. | |
| Sample code for NYUv2 can be found here: | |
| https://gist.github.com/ranftlr/a1c7a24ebb24ce0e2f2ace5bce917022 | |
| Sample code for KITTI can be found here: | |
| https://gist.github.com/ranftlr/45f4c7ddeb1bbb88d606bc600cab6c8d | |
| ### KITTI | |
| * Remove images from `/input/` and `/output_monodepth/` folders | |
| * Download `kitti_eval_dataset.zip` https://drive.google.com/file/d/1GbfMGuwg2VS06Vl75-_tB5FDj9EOrjl0/view?usp=sharing and unzip it in the `/input/` folder (or follow this repository https://github.com/cogaplex-bts/bts to get RGB and Depth images from list [eigen_test_files_with_gt.txt](https://github.com/cogaplex-bts/bts/blob/master/train_test_inputs/eigen_test_files_with_gt.txt) ) | |
| * Download [dpt_hybrid_kitti-cb926ef4.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_kitti-cb926ef4.pt) model and place it in the `/weights/` folder | |
| * Download [eval_with_pngs.py](https://raw.githubusercontent.com/cogaplex-bts/bts/5a55542ebbe849eb85b5ce9592365225b93d8b28/utils/eval_with_pngs.py) in the root folder | |
| * `python run_monodepth.py --model_type dpt_hybrid_kitti --kitti_crop --absolute_depth` | |
| * `python ./eval_with_pngs.py --pred_path ./output_monodepth/ --gt_path ./input/gt/ --dataset kitti --min_depth_eval 1e-3 --max_depth_eval 80 --garg_crop --do_kb_crop` | |
| Result: | |
| ``` | |
| Evaluating 697 files | |
| GT files reading done | |
| 45 GT files missing | |
| Computing errors | |
| d1, d2, d3, AbsRel, SqRel, RMSE, RMSElog, SILog, log10 | |
| 0.959, 0.995, 0.999, 0.062, 0.222, 2.575, 0.092, 8.282, 0.027 | |
| Done. | |
| ``` | |
| ---- | |
| ### NYUv2 | |
| * Remove images from `/input/` and `/output_monodepth/` folders | |
| * Download `nyu_eval_dataset.zip` https://drive.google.com/file/d/1b37uu-bqTZcSwokGkHIOEXuuBdfo80HI/view?usp=sharing and unzip it in the `/input/` folder (or follow this repository https://github.com/cogaplex-bts/bts to get RGB and Depth images from list [nyudepthv2_test_files_with_gt.txt](https://github.com/cogaplex-bts/bts/blob/master/train_test_inputs/nyudepthv2_test_files_with_gt.txt) ) | |
| * Download [dpt_hybrid_nyu-2ce69ec7.pt](https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid_nyu-2ce69ec7.pt) model (**or a new model** that is fine-tuned with slightly different hyperparameters [dpt_hybrid_nyu_new-217f207d.pt](https://drive.google.com/file/d/1Nxv2OiqhAMosBL2a3pflamTW39dMjaSp/view?usp=sharing) ) and place it in the `/weights/` folder | |
| * Download [eval_with_pngs.py](https://raw.githubusercontent.com/cogaplex-bts/bts/5a55542ebbe849eb85b5ce9592365225b93d8b28/utils/eval_with_pngs.py) in the root folder | |
| * `python run_monodepth.py --model_type dpt_hybrid_nyu --absolute_depth` | |
| (or **for new model** `python run_monodepth.py --model_type dpt_hybrid_nyu --absolute_depth --model_weights weights/dpt_hybrid_nyu_new-217f207d.pt` ) | |
| * `python ./eval_with_pngs.py --pred_path ./output_monodepth/ --gt_path ./input/gt/ --dataset nyu --max_depth_eval 10 --eigen_crop` | |
| Result (old model) - **from paper**: | |
| ``` | |
| Evaluating 654 files | |
| GT files reading done | |
| 0 GT files missing | |
| Computing errors | |
| d1, d2, d3, AbsRel, SqRel, RMSE, RMSElog, SILog, log10 | |
| 0.904, 0.988, 0.998, 0.109, 0.054, 0.357, 0.129, 9.521, 0.045 | |
| Done. | |
| ``` | |
| Result (new model): | |
| ``` | |
| GT files reading done | |
| 697 GT files missing | |
| Computing errors | |
| d1, d2, d3, AbsRel, SqRel, RMSE, RMSElog, SILog, log10 | |
| 0.905, 0.988, 0.998, 0.109, 0.055, 0.357, 0.129, 9.427, 0.045 | |
| Done. | |
| ``` | |