3D mapping techniques have a large variety of applications from entertainment to military and medical fields. However, obtaining a well-refined 3D model from a set of images without the usage of depth sensors is a big challenge. In the paper, we analyze the main components of the 3D reconstruction pipeline allowing us to get detailed models of outdoor objects from drones. In particular, we experiment with algorithms required for structure from motion and point cloud densification. We demonstrate that proper local feature extraction, matching and verification directly affect a final model quality. Analysis of two existing 3D reconstruction frameworks (MVE and COLMAP) is conducted. Initial experimental results are shown.

Outdoor Mapping Framework: from Images to 3D Model