DGSAC: Density Guided Sampling and Consensus

In this paper, we present an automatic multi-model fitting pipeline that can robustly fit multiple geometric models present in the corrupted and noisy data. Our approach can handle large data corruption and requires no user input, unlike most state-of-the-art approaches. The pipeline can be used as an independent block in many geometric vision applications like 3D reconstruction, motion and planar segmentation. We use residual density as the primary tool to guide hypothesis generation, estimate the fraction of inliers, and perform model selection. We show results for a diverse set of geometric models like planar homographies, fundamental matrices and vanishing points, which often arise in various computer vision applications. Despite being fully automatic, our approach achieves competitive performance compared to state-of-the-art approaches in terms of accuracy and computational time.