Efficient Dense Reconstruction from Video

We present a framework for efficient reconstruction of dense scene structure from video. Sequential structure-from-motion recovers camera information from video, providing only sparse 3D points. We build a dense 3D point cloud by performing full-frame tracking and depth estimation across sequences. First, we present a novel algorithm for sequential frame selection to extract a set of key frames with sufficient parallax for accurate depth reconstruction. Second, we introduce a technique for efficient reconstruction using dense tracking with geometrically correct optimisation of depth and orientation. Key frame selection is also performed in optimisation to provide accurate depth reconstruction for different scene elements. We test our work on benchmark footage and scenes containing local non-rigid motion, foreground clutter and occlusions to show comparable performance to state of the art techniques. We also show a substantial increase in speed on real world footage compared to existing methods, when they succeed, and successful reconstructions when they fail.