Incremental Estimation of Dense Depth Maps from Image Sequences

Abstract

The authors introduce a novel pixel-based (iconic) algorithm that estimates depth and depth uncertainty at each pixel and incrementally refines these estimates over time. They describe the algorithm for translations parallel to the image plane and contrast its formulation and performance to that of a feature-based Kalman filtering algorithm. They compare the performance of the two approaches by analyzing their theoretical convergence rates, by conducting quantitative experiments with images of a flat poster, and by conducting qualitative experiments with images of a realistic outdoor scene model. The results show that the method is an effective way to extract depth from lateral camera translations and suggest that it will play an important role in low-level vision.