We propose DFPNet -- an unsupervised, joint learning system for monocular
Depth, Optical Flow and egomotion (Camera Pose) estimation from monocular image
sequences. Due to the nature of 3D scene geometry these three components are
coupled. We leverage this fact to jointly train all the three components in an
end-to-end manner. A single composite loss function -- which involves image
reconstruction-based loss for depth & optical flow, bidirectional consistency
checks and smoothness loss components -- is used to train the network. Using
hyperparameter tuning, we are able to reduce the model size to less than 5%
(8.4M parameters) of state-of-the-art DFP models. Evaluation on KITTI and
Cityscapes driving datasets reveals that our model achieves results comparable
to state-of-the-art in all of the three tasks, even with the significantly
smaller model size.

DFPNet 是一种联合学习系统，通过单眼图像序列估计单眼景深、光流和自身运动（相机姿态），使用单一的复合损失函数进行训练，并使用超参数调整来使模型的大小小于目前市场上同类模型的 5％以下。在 KITTI 和 Cityscapes 驾驶数据集上进行评估，结果表明我们的模型在所有三个任务上的表现都可以媲美目前市场上同类模型的表现，即使模型大小明显更小。