Monocular adaptive inverse depth filtering algorithm based on Gaussian model

This paper presents an adaptive filtering algorithm for monocular depth estimation. This is a geometric calculation method under the assumption that the inverse depth conforms to the Gaussian distribution. In the inverse depth update, the average value of the historical stable points is introduced to smooth the output, and the depth uncertainty of the unit pixel offset is calculated to eliminate the estimation error. In the similarity search along the epipolar line, the normalized cross-correlation and gradient are used as joint metrics. Finally, the effectiveness of the algorithm is verified through experiments, and it can get a better trade-off between performance and time.