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Advances in neural networks enable tackling complex computer vision taskssuch as depth estimation of outdoor scenes at unprecedented accuracy. Promisingresearch has been done on depth estimation. However, current efforts arecomputationally resource-intensive and do not consider the resource constraintsof autonomous devices, such as robots and drones. In this work, we present afast and battery-efficient approach for depth estimation. Our approach devisesmodel-agnostic curriculum-based learning for depth estimation. Our experimentsshow that the accuracy of our model performs on par with the state-of-the-artmodels, while its response time outperforms other models by 71%. All codes areavailable online at https://github.com/fatemehkarimii/LightDepth.

LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning