  Advances in neural networks enable tackling complex computer vision tasks
such as depth estimation of outdoor scenes at unprecedented accuracy. Promising
research has been done on depth estimation. However, current efforts are
computationally resource-intensive and do not consider the resource constraints
of autonomous devices, such as robots and drones. In this work, we present a
fast and battery-efficient approach for depth estimation. Our approach devises
model-agnostic curriculum-based learning for depth estimation. Our experiments
show that the accuracy of our model performs on par with the state-of-the-art
models, while its response time outperforms other models by 71%. All codes are
available online at https://github.com/fatemehkarimii/LightDepth.
