Update README.md

README.md

@@ -7,23 +7,13 @@ The repo consistst of the following parts:
 
 - `script.py` - the main file, which is run by the competition space. It should produce `submission.parquet` as the result of the run.
 - `hoho.py` - the file for parsing the dataset at the inference time. Do NOT change it.
-- `handcrafted_solution.py` - contains the actual implementation of the algorithm
+- `feature_solution.py` - contains the implementation of ours method
 - other `*.py` files - helper i/o and visualization utilities
 - `packages/` - the directory to put python wheels for the custom packages you want to install and use. 
 
 ## Solution description
 
-The solution is simple. 
-
-1. Using provided (but noisy) semantic segmentation called `gestalt`, it takes the centroids of the vertex classes - `apex` and `eave_end_point` and projects them to 3D using provided (also noisy) monocular depth. 
-2. The vertices are connected using the same segmentation, by checking for edges classes to be present - `['eave', 'ridge', 'rake', 'valley']`. 
-3. All the "per-image" vertex predictions are merged in 3D space if their distance is less than threshold.
-4. All vertices, which have zero connections, are removed. 
-
-
-## Example on the training set
-
-See in [notebooks/example_on_training.ipynb](notebooks/example_on_training.ipynb)
+The solution is based on 2D features and line matching.
 
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 license: apache-2.0