  Camera motion estimation is a key technique for 3D scene reconstruction and
Simultaneous localization and mapping (SLAM). To make it be feasibly achieved,
previous works usually assume slow camera motions, which limits its usage in
many real cases. We propose an end-to-end 3D reconstruction system which
combines color, depth and inertial measurements to achieve robust
reconstruction with fast sensor motions. Our framework extends Kalman filter to
fuse the three kinds of information and involve an iterative method to jointly
optimize feature correspondences, camera poses and scene geometry. We also
propose a novel geometry-aware patch deformation technique to adapt the feature
appearance in image domain, leading to a more accurate feature matching under
fast camera motions. Experiments show that our patch deformation method
improves the accuracy of feature tracking, and our 3D reconstruction
outperforms the state-of-the-art solutions under fast camera motions.
