Structure from Motion (SfM) is the cornerstone of 3D reconstruction and visualization of SLAM. Existing deep learning approaches formulate problems by restoring absolute pose ratios from two consecutive frames or predicting a depth map from a single image, both of which are unsuitable problems. In order to solve this maladaptation problem and further tap the potential of neural networks in SfM, this paper proposes a new optimization model for deep motion structure recovery based on recurrent neural networks. The model consists of two architectures based on depth and posture estimation of costs, and is constantly iteratively updated alternately to improve both systems. The neural optimizer designed here tracks historical information during iterations to minimize feature metric cost update depth and camera poses. Experiments show that the optimization model of deep motion structure recovery in this paper is superior to the previous method, effectively reducing the cost of feature-metric, while refining depth and poses.
Remote sensing image processing has been widely used in environmental monitoring, terrain survey, military investigation, disaster early warning, and other fields. The remote sensing images matching is a key step, and the accuracy and real-time of the alignment have a great impact on these applications. Due to the high resolution and complexity of remote sensing images, scale-invariant feature transformation (SIFT) algorithm has the problems of high computational complexity and poor matching effect. Adaptive threshold adjustment algorithm has proposed in the remote sensing image matching, but minor changes in the contrast threshold can bring about drastic changes in the image matching quality, which will affect applications such as monitoring, measurement, and early warning. To improve the matching quality of a SIFT detector, an adaptive contrast threshold SIFT method based on image complexity calculation (CACT-SIFT) is proposed. The CACT-SIFT finds two contrast thresholds for the target image and the reference image, respectively, and achieves the target by minimizing the proposed criteria Cik. Experiments show that the method can be applied to the matching of remote sensing images. The information of reference images and target images can be detected at the same time, and the key points can be extracted in a robust way, with better accuracy and real-time accuracy of the alignment.
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