documentation

docs/MonStereo.md

@@ -138,6 +138,8 @@ We provide evaluation on KITTI in the eval section. Txt files for MonStereo are
 
 `python -m monstereo.run eval --dir_ann <directory of pifpaf annotations> --model data/models/ms-200710-1511.pkl  --generate`
 
+<img src="quantitative_mono.png" width="500"/>
+
 ### Relative Average Precision Localization (RALP-5%)
 We modified the original C++ evaluation of KITTI to make it relative to distance. We use **cmake**.
 To run the evaluation, first generate the txt files with:

docs/MonoLoco++.md

@@ -189,8 +189,6 @@ and save them into `data/kitti/3dop`
 and save them into `data/kitti/monodepth`
 * **GeometricalBaseline**: A geometrical baseline comparison is provided. 
 
-Download the model for monoloco
-
 The average geometrical value for comparison can be obtained running:
 ```
 python -m monstereo.run eval 
@@ -202,6 +200,11 @@ python -m monstereo.run eval
 
 To include also geometric baselines and MonoLoco, add the flag ``--baselines``
 
+<img src="quantitative_mono.png" width="500"/>
+
+Adding the argument `save`, a few plots will be added including 3D localization error as a function of distance:
+<img src="results.png" width="500"/>
+
 ### Activity Estimation (Talking)
 Please follow preprocessing steps for Collective activity dataset and run pifpaf over the dataset images.
 Evaluation on this dataset is done with models trained on either KITTI or nuScenes. 

docs/SocialDistancing.md

@@ -1,8 +0,0 @@
-# Work in progress
-A detailed readme is currently under development 
-and will be released soon. For h
-
-For a Beta version, we suggest to take a look at the branch update, 
-from which you can also reproduce the latest quantitative results.
-
-Thank you for your comprehension and patience.