This code accompanies the paper "Learning to map surrounding vehicles into bird's eye view using synthetic data".
It contains the code for loading data and pre-trained SDPN model proposed in the paper.
Script entry-point is in main.py.
When main.py is run, pretrained weights are automatically downloaded and injected in the model.
Model is then used to perform and inference on a sample data, mapping a car from the dashboard camera view to the bird's eye view of the scene. If everything works correctly, the output should look like this.
The code was developed with the following configuration:
- python 2.7.11
- numpy 1.11.2
- opencv 3.1.0
- Theano 0.9.0.dev3
- Keras 1.1.2
Other configuration will reasonably work, but have never been explicitly tested.
In this repository only one example is provided, to the end of verifying that the model is working correctly.
The whole dataset, which comprises more than 1M couples of bounding boxes, can be found here.
To get an idea of how the data look like you can check this video.