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6 DoF pose estimation problem aims to estimate the rotation and translation parameters between two coordinates, such as object world coordinate and camera world coordinate. Although some advances are made with the help of deep learning, how to full use scene information is still a problem. Prior works tackle the problem by pixel-wise feature fusion but need to randomly select numerous points from images, which can not satisfy the demands of fast inference simultaneously and accurate pose estimation. In this work, we present a novel deep neural network based on dynamic keypoint selection designed for 6DoF pose estimation from a single RGBD image. Our network includes three parts, instance semantic segmentation, edge points detection and 6DoF pose estimation. Given an RGBD image, our network is trained to predict pixel category and the translation to edge points and center points. Then, a least-square fitting manner is applied to estimate the 6DoF pose parameters. Specifically, we propose a dynamic keypoint selection algorithm to choose keypoints from the foreground feature map. It allows us to leverage geometric and appearance information. During 6DoF pose estimation, we utilize the instance semantic segmentation result to filter out background points and only use foreground points to finish edge points detection and 6DoF pose estimation. Experiments on two commonly used 6DoF estimation benchmark datasets, YCB-Video and LineMoD, demonstrate that our method outperforms the state-of-the-art methods and achieves significant improvements over other same category methods time efficiency. • A simple but effective dynamic keypoint selection algorithm that leverages texture and geometry information of object. • A filtering background points algorithm significantly improves pose estimation time efficiency. • State-of-the-art 6DoF pose estimation performance on the YCB-Video and LineMOD datasets. [ABSTRACT FROM AUTHOR]

Accurate detection of crystal defects from microstructure micrographs is essential for evaluating the mechanical and functional properties of metallic materials. Emerging deep learning techniques have great potential for application in crystal defects detection, but the excellent detection accuracy requires domain knowledge and micrograph datasets. Here we target to detect and distinguish nanoscale dislocation loops (DLs) and stacking fault tetrahedrons (SFTs) in transmission electronic microscope (TEM) micrographs. The micrograph datasets of crystal defects derive from copper prepared by rapid quenching and post-annealing. Applying standard U-Net architecture, the maximum Mean Intersection over Union (mIOU max) of 3-class model is 54.29%, while it is 73.81% and 74.67% of DL 2-class and SFT 2-class model. Results suggest that excellent detection accuracy of deep learning model could be achieved by learning features from specific defect datasets. These findings provide a new strategy for accurate detection of crystal defects in the field of material engineering. Accurate detection of crystal defects from microstructure micrographs is essential for evaluating the mechanical and functional properties of metallic materials. Emerging deep learning techniques have great potential for application in crystal defects detection, but the excellent detection accuracy requires domain knowledge and micrograph datasets. Here we target to detect and distinguish nanoscale dislocation loops (DLs) and stacking fault tetrahedrons (SFTs) in transmission electronic microscope (TEM) micrographs. The micrograph datasets of crystal defects derive from copper prepared by rapid quenching and post-annealing. Applying standard U-Net architecture, the maximum Mean Intersection over Union (mIOU max) of 3-class model is 54.29%, while it is 73.81% and 74.67% of DL 2-class and SFT 2-class model. Results suggest that excellent detection accuracy of deep learning model could be achieved by learning features from specific defect datasets. These findings provide a new strategy for accurate detection of crystal defects in the field of material engineering. [Display omitted] • Quenching coupled with post-annealing techniques is an efficient strategy for introducing vacancy defects in FCC metals. The subsequent quantitative TEM characterization supports the construction of vacancy defect micrograph dataset, which is suitable for training deep learning semantic segmentation models. • Deep learning semantic segmentation technology is successfully applied on the analysis of nanoscale defects in TEM micrographs. Results suggest that constructing a defect micrograph dataset containing clear microstructure features can further improve the comprehensive performance of deep learning models. [ABSTRACT FROM AUTHOR]