TensorRT Acceleration and SuperGlue Feature Matching in SFM: Performance Improvement and Dense 3D Reconstruction

Currently, in the field of computer vision and 3D reconstruction, the pursuit of perfor mance improvement and efficiency optimization has been a common focus of researchers and engineers. To cope with the growing application demands, especially in the fields of autonomous driving and virtual reality, the need for high-speed and high-precision 3D reconstruction has be come more and more urgent.SFM (Structure from Motion) is a technique in the field of computer vision and computer graphics for reconstructing the geometric structure of a 3D scene from mul tiple images. However, the traditional SFM technique, which uses purely visual geometric com putation for feature extraction and matching, not only takes a long time to compute, but also the matching effect is not ideal and the accuracy is low, thus affecting the effect of image 3D recon struction. Therefore, how to improve the effect and efficiency of image 3D reconstruction is a problem to be solved by the technicians in the field. In this context, the research design in this paper is based on opensfm to improve. First, we introduce SuperGlue feature matching technique to solve the feature matching problem in SFM system.SuperGlue technique combines deep learn ing and geometric geometry information, which can realize more accurate and robust feature matching in complex environment, thus improving the accuracy and stability of 3D reconstruc tion. By integrating SuperGlue technology into the SFM system, we can better cope with the challenges of feature matching in various scenarios, thus further improving the performance and reliability of the SFM system. We then introduced the TensorRT acceleration library to improve the inference speed and efficiency of SuperPoint and SuperGlue models.TensorRT can optimize neural network models and leverage the parallel computing power of GPUs to achieve high performance inference, which significantly improves the real-time performance of the SFM sys tem. We cross-fertilize techniques from the fields of deep learning and accelerated model infer ence as well as SFM techniques for traditional view geometry computation to obtain a fast, high quality technique for dense 3D reconstruction.