Abstract
With the growing use of smartphones, generating depth-map to accompany user acquisitions is becoming increasingly important for both manufacturers and consumers. Depth from Small Motion (DfSM) has been shown to be suitable approach since depth-maps can be generated with minimal effort such as handshaking motion, and without knowing camera calibration parameter. Direct porting of a desktop PC implementation of DfSM on mobile devices propose a major challenge due to its long execution time. The algorithm has been designed to run on desktop computers that have higher energy-efficient optimizations compared to mobile device with slower processors.
In this paper, we investigate ways to speed up the DfSM algorithm to run faster on mobile devices. After porting the algorithm to the mobile platform, we applied several optimization techniques using mobile CPU-GPU co-processing by exploiting OpenCL capabilities. We evaluate the impact of our optimizations on performance, memory allocation, and demonstrate about 3\(\times \) speedup over mobile CPU implementation. We also show the portability of our optimizations by running on two different ANDROID devices.
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Fasogbon, P., Aksu, E., Heikkilä, L. (2019). Demo: Accelerating Depth-Map on Mobile Device Using CPU-GPU Co-processing. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11678. Springer, Cham. https://doi.org/10.1007/978-3-030-29888-3_7
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