Frame selection to accelerate Depth from Small Motion on smartphones
Peter Fasogbon, Lasse Heikkilä, Emre Aksu · 2019
Depth from Small Motion (DfSM) is particularly interesting for smartphone devices because it makes it possible to get depth information with minimal user effort and cooperation. The state of art method requires about 30 images for the optimization to converge fast and produce accurate depth-map. As the use of high number of frames contribute to long execution time and huge memory allocation, we propose a frame selection strategy using Inertial Measurement Unit (IMU) and image based analysis. As a result, only 5 frames with appropriate viewpoint from the reference one are used for the depth-map generation. Full experiment is done on an Android platform using optimized version of the proposed method with CPU-GPU co-processing under OpenCL. We are able to provide accurate camera parameters and depth-map estimates using only these selected frames.