-> This project aims at studying the fusion of depth estimates coming from a convolutional neural network and stereo vision system (https://www.stereolabs.com/). The neural network is trained online with supervisory input coming from a sparse (stereo) depth map. Ideally, the final system should be able to run on real-time so that can be used for indoor navigation on a drone or a MAV. The system is being implemented on the NVIDIA TX1 Development board (https://www.nvidia.com/object/jetson-tx1-module.html).
-> This repository will be used to keep track of the progress, which means that the code might not always be fully optimized, well documented or fully functional. There are a few important dependencies:
- OpenCV
- ZED SDK
- CUDA
- Caffe
The program receives as input a pre-trained CNN, which will be retrained online, and two images, which are fed to a stereo vision algorithm and to the CNN. The final output is a depth map which makes uses of both estimations to generate one with lower error than the individual estimations.
- Extracting depth maps from CNN
- Extracting depth maps from ZED camera
- Implementing merging algorithm presented in Mancini et al.
- Implement error functions class
- Generate Depth Dataset from indoor footages at TU DELFT
- Implementing offline learning strategy
- Implementing SSL strategy
The CNNs models can be extracted from https://sira.diei.unipg.it/supplementary/ral2016/extra.html
- Facil, J. et al - Deep Single and Direct Multi-View Depth Fusion
- Mancini, M. et al - Towards Domain Independence for Learning-Based
- Eigen et al - Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecture