S23DR-P2R

Handcrafted solution example for the S23DR competition

This repo provides an example of a simple algorithm to reconstruct wireframe and submit to S23DR competition.

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
• 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

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license: apache-2.0