Real-time 3D reconstruction has always been a hot problem in mobile robotics. However, feature extraction algorithms used in traditional 3D reconstruction systems cannot work stably in challenging environments such as low-textured areas. The feature extraction method based on deep learning has higher accuracy and stability than traditional methods, but the complicated network structure leads to the lack of real-time performance. To overcome the limitations, a real-time 3D reconstruction system using multi-task feature extraction network and surfel is proposed. To enhance the stability and accuracy, we design a simplified convolutional neural network to extract feature. Moreover, surfel model is employed to implement the fusion and optimization of 3D point cloud. According to the experiments on the public dataset and real environments, the proposed system can run on Robot Operating System in real time, maintain high pose estimation accuracy in challenging scenes, and complete precise 3D reconstruction. The overall performance of the proposed system is better than that of the traditional 3D reconstruction system.

中文翻译：

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使用多任务特征提取网络和surfel的实时3D重建系统

实时 3D 重建一直是移动机器人领域的热点问题。然而，传统 3D 重建系统中使用的特征提取算法在低纹理区域等具有挑战性的环境中无法稳定工作。基于深度学习的特征提取方法比传统方法具有更高的准确率和稳定性，但网络结构复杂导致实时性不足。为了克服这些限制，提出了一种使用多任务特征提取网络和面元的实时 3D 重建系统。为了提高稳定性和准确性，我们设计了一个简化的卷积神经网络来提取特征。此外，采用surfel模型来实现3D点云的融合和优化。根据在公共数据集和真实环境上的实验，所提出的系统可以在机器人操作系统上实时运行，在具有挑战性的场景中保持较高的姿态估计精度，并完成精确的 3D 重建。所提出的系统的整体性能优于传统的 3D 重建系统。