Attention-Based Dense Decoding Network for Monocular Depth Estimation

Attention-Based Dense Decoding Network for Monocular Depth Estimation

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Depth estimation is a classic computer vision task and provides rich representation of objects and environment.In recent years, the performance of end-to-end depth estimation has been significantly improved.However, the stack of convolutions and pooling operations result in losing COCONUT BREEZE SHAMPOO W CONDITIONER local detail spatial information, which is extremely important to monocular depth estimation.In order to overcome this problem, in this work, we propose an encoder-decoder framework with skip connections.Based on the self-attention mechanism, we apply the channel-spatial attention module as a transition layer, which captures the depth and spatial positional relationship and improves the presentation ability of channel and space.

Then we propose a dense decoding module to make full use of the attention features 12oz Ripstop of different scale ranges in the decoding process.It achieves a more massive and denser receptive field while obtaining multi-scale information.Finally, a novel distance-aware loss is introduced to predict more meticulous edges and local details in the distance.Experiments demonstrate that the proposed method outperforms the state-of-the-art on KITTI and NYU Depth V2 datasets.