Kavli Affiliate: Zheng Zhu
| First 5 Authors: Chaoqiang Zhao, Youmin Zhang, Matteo Poggi, Fabio Tosi, Xianda Guo
| Summary:
Self-supervised monocular depth estimation is an attractive solution that
does not require hard-to-source depth labels 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.
| Search Query: ArXiv Query: search_query=au:”Zheng Zhu”&id_list=&start=0&max_results=10