轻量化自监督单目深度估计

Lightweight Self-supervised Monocular Depth Estimation

目前,大多数的增强现实和自动驾驶应用不仅会使用到深度网络估计的深度信息,还会使用到位姿网络估计的位姿信息.将位姿网络和深度网络同时集成到嵌入式设备上,会极大地消耗内存.为解决这一问题,提出一种深度网络和位姿网络共用特征提取器的方法,使模型保持在一个轻量级的尺寸.此外,通过带有线性结构的深度可分离卷积轻量化深度网络,使网络在不丢失过多细节信息前提下还可获得更少的参数量.最后,通过在KITTI数据集上的实验表明,与同类算法相比,该位姿网络和深度网络参数量只有的35.33 MB.同时,恢复深度图的平均绝对误差也保持在0.129.

Currently,most augmented reality and autonomous driving applications use not only the depth information estimated by the depth network but also the pose information estimated by the pose network.Integrating both the pose network and the depth network into an embedded device can be extremely memory-consuming.In view of this problem,a method of the depth and pose networks sharing feature extractors is proposed to keep the model at a lightweight size.In addition,the depth-separable convolutional lightweight depth network with linear structure allows the network to obtain fewer parameters without losing too much detailed information.Finally,experiments on the KITTI dataset show that compared with the algorithms of the same type,the size of the pose and deep network parameters is only 35.33 MB.At the same time,the average absolute error of the restored depth map is also maintained at 0.129.