MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
By Chaoqiang Zhao, Youming Zhang, Matteo Poggi, Fabio Tosi, Xianda Guo, Zheng Zhu, Guan Huang, Yang Tang, Stefano Mattoccia. International Conference on 3D Vision, 2022
309 citations recorded by the source corpus.
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth la-bels for training. Convolutional neural networks (CNNs) have recently achieved great success in this task. However, their limited receptive field constrains existing network architectures to reason only locally, dampening the effectiveness of the self-supervised paradigm. In the light of the recent successes achieved by Vision Transformers (ViTs), we propose MonoViT, a brand-new framework combining the global reasoning enabled by ViT models with the flexibility of self-supervised monocular depth estimation. By combining plain convolutions with Transformer blocks, our model can reason locally and globally, yielding depth prediction at a higher level of detail and accuracy, allowing MonoViT to achieve state-of-the-art performance on the established KITTI dataset. Moreover, MonoViT proves its superior generalization capacities on other datasets such as Make3D and DrivingStereo. Source code available at https://github.com/zxcqlf/MonoViT
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/a541b67926de2e0964cb1f3922e2d5dc54d26b28
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation (2022, methodology): “The latest MonoViT-tiny [43] runs the slowest due to its parallel blocks and multiple layers of self-attention.”