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

Real-time Indoor Scene Reconstruction with RGBD and Inertia Input