Sparse LIDAR Measurement Fusion with Joint Updating Cost for Fast Stereo Matching

The complementary virtues of active and passive depth sensors inspire the LIDAR-Stereo fusion for enhancing the accuracy of stereo matching. However, most of the fusion based stereo matching algorithms have exploited dense LIDAR priors with single fusion methodology. In this paper, we intend to break these fetters, utilizing sparse LIDAR priors with multi-step fusion strategy for obtaining accurate disparity estimation more efficiently. At first, random sparse sampling LIDAR depth measurements are provided in Naive Fusion for updating the matching cost of Semi-Global Matching (SGM). Then Neighborhood Based Fusion is performed based on the former step for further updating the cost. Subsequently, Diffusion Based Fusion is utilized to update both the cost and disparities. At last, Tree Filtering is applied for removing speckle outliers and smoothing disparities. Performance evaluations on various stereo data sets demonstrate that the proposed algorithm outperforms other most challenging stereo matching algorithms significantly with approximately real-time implementation efficiency. Furthermore, it is worth pointing out that our proposal surprisingly possesses one of the top ten performances on Middlebury v.3 online evaluation system even if it has not been adopted any learning-based techniques.