ARCHITECTURE OF MobDepth:
- Input - Camera Frame (480,640,3) , Output - Depth Map (240,320,1)
- Design Choices:
Real-Time Estimation - As the model needs to predict in real time, thus a light-weight architecture is required.
As MobileNet uses least Number of Parameters without compromising on the Generalization, Thus it is used as Encoder.
Generalization and High-Quality Depth Map - MobileNet is Pre-trained on ImageNet Dataset and including Skip-Connections make it more generalizable, removing the problem of Vanashing Gradient.
SOME MORE RESULTS (on MobDepth(with Skip-Connections)):
AUGMENTATION OF SIMULATOR DATA (coppeliaSim):
To integrate the component with robocomp, model should give high quality depth map on Simulator data as well, obained using coppeliaSim. Thus, NYU dataset is Augmented with Simulator Data.
Process of Data Collection of Simulator Data can be found in Data-Collector
FINE-TUNING USING COLLECTED SIMULATOR DATA:
- The Pre-Trained Model (“MobDepth (with Skip-Connections)”) is trained further on collected simulator data to give high quality depth map in simulated world.
RESULTS BEFORE AND AFTER FINE-TUNING: