Depth estimation is crucial for autonomous systems, and the shift towards monocular cameras has led to the development of METER, a novel vision transformer architecture. The proposed method outperforms previous works on benchmark datasets, showcasing advancements in deep learning algorithms for depth estimation.
Recent models aim to enable depth perception using single RGB images on deep vision transformer architectures. The paper presents METER, which achieves superior results over benchmark datasets NYU Depth v2 and KITTI. By integrating transformers blocks and convolutional operations, METER balances computational complexity and hardware constraints effectively.
The study also focuses on a balanced loss function to enhance pixel estimation and image detail reconstruction. Additionally, a new data augmentation strategy improves overall predictions. The proposed network structure combines an encoder-decoder design with specific components tailored for efficient monocular depth estimation.
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by L. Papa,P. R... at arxiv.org 03-14-2024
https://arxiv.org/pdf/2403.08368.pdfDeeper Inquiries