Lightweight Monocular Depth Estimation via Token-Sharing Transformer
Dong-Jae Lee Jae Young Lee Hyounguk Shon Eojindl Yi Yeong-Hun Park Sung-Sik Cho Junmo Kim
Depth estimation is an important task in various robotics systems and
applications. In mobile robotics systems, monocular depth estimation is
desirable since a single RGB camera can be deployable at a low cost and compact
size. Due to its significant and growing needs, many lightweight monocular
depth estimation networks have been proposed for mobile robotics systems. While
most lightweight monocular depth estimation methods have been developed using
convolution neural networks, the Transformer has been gradually utilized in
monocular depth estimation recently. However, massive parameters and large
computational costs in the Transformer disturb the deployment to embedded
devices. In this paper, we present a Token-Sharing Transformer (TST), an
architecture using the Transformer for monocular depth estimation, optimized
especially in embedded devices. The proposed TST utilizes global token sharing,
which enables the model to obtain an accurate depth prediction with high
throughput in embedded devices. Experimental results show that TST outperforms
the existing lightweight monocular depth estimation methods. On the NYU Depth
v2 dataset, TST can deliver depth maps up to 63.4 FPS in NVIDIA Jetson nano and
142.6 FPS in NVIDIA Jetson TX2, with lower errors than the existing methods.
Furthermore, TST achieves real-time depth estimation of high-resolution images
on Jetson TX2 with competitive results.