Transformers Solve the Limited Receptive Field for Monocular Depth Prediction

• URL: http://arxiv.org/abs/2103.12091v1
• Date: Mon, 22 Mar 2021 18:00:13 GMT
• Title: Transformers Solve the Limited Receptive Field for Monocular Depth Prediction
• Authors: Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
• Abstract summary: We propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. This is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels.
• Score: 82.90445525977904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation. Transformers, initially designed for natural language processing tasks, have emerged as alternative architectures with innate global self-attention mechanisms to capture long-range dependencies. In this paper, we propose TransDepth, an architecture which benefits from both convolutional neural networks and transformers. To avoid the network to loose its ability to capture local-level details due to the adoption of transformers, we propose a novel decoder which employs on attention mechanisms based on gates. Notably, this is the first paper which applies transformers into pixel-wise prediction problems involving continuous labels (i.e., monocular depth prediction and surface normal estimation). Extensive experiments demonstrate that the proposed TransDepth achieves state-of-the-art performance on three challenging datasets. The source code and trained models are available at https://github.com/ygjwd12345/TransDepth.

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