Mobiscan3D: A Low Cost Framework for Real Time Dense 3D Reconstruction on Mobile Devices

In this paper we propose a computationally inexpensive framework for dense 3D reconstruction on smart device platforms leveraging the feed from motion sensors. In contrast to other methods, we solely rely on the motion sensors to compute pair wise epipolar relationships. In particular, camera positions are obtained only through noisy mobile sensor data which is further optimized globally using iterative reweighted least squares. Rotations are also obtained using mobile sensors. Our method of obtaining camera motion reduce the processing time of the entire pipeline. We further use pair wise epipolar relationships along with normalized cross correlation and gradient information in a pair of images to obtain dense correspondences. The calibrations and correspondences are accurate enough for triangulation which in turn serve as a good initializer for final global bundle adjustment in near real time. Experimental results show that our method works reliably well for both indoor and outdoor scenes of different size and shapes without even utilizing mobile GPU.