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首页> 外文期刊>Journal of visual communication & image representation >3D reconstruction with auto-selected keyframes based on depth completion correction and pose fusion
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3D reconstruction with auto-selected keyframes based on depth completion correction and pose fusion

机译：基于深度完成校正和姿态融合的三维重建与自动选择的关键帧

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摘要

Dense 3D reconstruction is required for robots to safely navigate or perform advanced tasks. The accurate depth information of the image and its pose are the basis of 3D reconstruction. The resolution of depth maps obtained by LIDAR and RGB-D cameras is limited, and traditional pose calculation methods are not accurate enough. In addition, if each image is used for dense 3D reconstruction, the dense point clouds will increase the amount of calculation. To address these issues, we propose a 3D reconstruction system. Specifically, we propose a depth network of contour and gradient attention, which is used to complete and correct depth maps to obtain high-resolution and high-quality depth maps. Then, we propose a method of fusion of traditional algorithms and deep learning for pose estimation to obtain accurate localization results. Finally, we adopt the method of autonomous selection of keyframes to reduce the number of keyframes, the surfel-based geometric reconstruction is performed to reconstruct the dense 3D environment. On the TUM RGB-D, ICL-NIUM, and KITTI datasets, our method significantly improves the quality of the depth maps, the localization results, and the effect of 3D reconstruction. At the same time, we have also accelerated the speed of 3D reconstruction.
机译：机器人需要致密3D重建，以安全地导航或执行高级任务。图像的精确深度信息及其姿势是3D重建的基础。 LIDAR和RGB-D相机获得的深度图的分辨率有限，传统的姿势计算方法不够准确。另外，如果每个图像用于密集的3D重建，则密集点云将增加计算量。要解决这些问题，我们提出了一个3D重建系统。具体而言，我们提出了一种深度网络的轮廓和梯度注意，用于完成和校正深度图以获得高分辨率和高质量的深度图。然后，我们提出了一种融合传统算法和深度学习的方法，以获得准确的本地化结果。最后，我们采用自主选择的关键帧的方法来减少关键帧的数量，执行基于冲浪的几何重构以重建密集的3D环境。在Tum RGB-D，ICL-N和Kitti Datasets上，我们的方法显着提高了深度图的质量，定位结果和3D重建的效果。与此同时，我们还加速了3D重建的速度。

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