基于多约束优化的图像深度信息估计

Depth information estimation based on multi-constraint optimization

提出了一种基于深度学习的多约束框架,用于从单目视频中预测深度图。该框架不仅通过最小化光度误差来对网络进行优化,还引入了匹配点对约束和极线约束来弥补光度误差在无纹理区域和光照变化情况下的不稳定性。此外,该框架还加入了非连续图像之间的约束来改善模型的表现。通过与其他深度估计方法进行对比分析,结果表明:该框架可以提高深度预测的准确性,增强了模型在处理无纹理区域和光照变化时的鲁棒性。

We propose a multi-constraint framework based on deep learning to predict depth maps from monocular videos. The framework not only optimizes the network by minimizing the photometric error, but also introduces the matching point constraints and epipolar constraints to compensate for the instability of the photometric error in texture-less regions and varying illumination conditions. In addition, the framework adds the constraints between non-adjacent frames to the model to improve its performance. Compared with other methods,our method can improve the accuracy of depth prediction and enhance the robustness of the model in handling texture-less regions and varied illumination.