国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

A parallelized implementation of automatic differentiation that derives from the problem of bundle adjustment is proposed, which is based on OpenCL parallel computing framework. Reverse mode of automatic differentiation is more efficient to compute the derivatives of functions with multiple parameters, which is the case of computing the Jacobian matrix in bundle adjustment problem. Under the framework of OpenCL, C/C++ style function construction and topological sorting based computational sequence generation are implemented on the host side. On the device side, function values and derivatives are computed in parallel according to computational sequence. Large scale bundle adjustment datasets are used to evaluate the proposed implementation. The result shows that our implementation runs about 3.6 times faster than Ceres Solver which utilizes OpenMP parallel programming model.