With the frequent use of self-supervised monocular depth estimation in
robotics and autonomous driving, the model's efficiency is becoming
increasingly important. Most current approaches apply much larger and more
complex networks to improve the precision of depth estimation. Some researchers
incorporated Transformer into self-supervised monocular depth estimation to
achieve better performance. However, this method leads to high parameters and
high computation. We present a fully convolutional depth estimation network
using contextual feature fusion. Compared to UNet++ and HRNet, we use
high-resolution and low-resolution features to reserve information on small
targets and fast-moving objects instead of long-range fusion. We further
promote depth estimation results employing lightweight channel attention based
on convolution in the decoder stage. Our method reduces the parameters without
sacrificing accuracy. Experiments on the KITTI benchmark show that our method
can get better results than many large models, such as Monodepth2, with only 30
parameters. The source code is available at
this https URL

我们提出了一种全卷积深度估计网络，利用上下文特征融合，采用高分辨率和低分辨率特征来保留小目标和高速移动物体的信息，进一步在解码器阶段采用基于卷积的轻量级通道注意力来优化深度估计结果，降低参数数量同时保持准确性。