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2021 International Conference on 3D Vision (3DV)
Deep neural networks have recently thrived on single image depth estimation. That being said, current developments on this topic highlight an apparent compromise between accuracy and network size. This work proposes an accurate and lightweight framework for monocular depth estimation based on a self-attention mechanism stemming from salient point detection. Specifically, we utilize a sparse set of keypoints to train a FuSaNet model that consists of two major components: Fusion-Net anddoi:10.1109/3dv53792.2021.00033 fatcat:ajhxad4pwbdwla3gagjtqcqtka