# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 1 Introduction  
Multi-view 3D reconstruction represents a crucial research avenue within the field of computer vision [1] and has significant applications in areas such as medical CT imaging, cultural heritage preservation, robotic visual localization, tunnel surveying, industrial inspection, and object digitization [2; 3; 4; 5; 6]. Fundamentally, three-dimensional reconstruction is the inverse process of camera imaging. It involves the transformation of the relationship between spatial three-dimensional points and two-dimensional image pixel points. Thisinformation is analyzed and integrated to calculate the spatial positioning of an object from various angles, ultimately yielding a three-dimensional model of the object. In the three-dimensional reconstruction of campus status, traditional algorithms depend on the exhaustive matching of feature descriptors from corresponding image pairs. This dependency considerably diminishes the efficiency of matching algorithms. Moreover, the various data acquisition methods employed in image reconstruction complicate the integration of all projections of an object across each image. The algorithms utilize a pair-wise matching strategy for feature matching during the reconstruction phase to facilitate subsequent reconstruction tasks. This approach not only significantly increases processing time but also introduces ambiguity in matching, which negatively impacts position estimation. Even prominent open-source solutions, such as COLMAP [7] and Bundler [8], face difficulties in overcoming these challenges. By concentrating on the three-dimensional reconstruction of small to medium-sized campus objects, this paper seeks to improve model completeness, point cloud density, and reconstruction accuracy based on the previously mentioned research background and current state. The following summarizes this paper's primary research contributions:  
1. To address the issues of low correct point pair matching and accuracy inherent in traditional feature matching algorithms, this study proposes an enhanced RANSAC optimization framework that integrates SIFT feature extraction, KD-Tree accelerated matching and adaptive threshold adjustment. The core concept of the automatic threshold adjustment algorithm is to dynamically determine the threshold based on the statistical properties of the data rather than relying on fixed settings. In each iteration of the RANSAC algorithm, after the preliminary model estimation, it is necessary to compute the residuals of all data points based on the current model and dynamically adjust the threshold to ensure that the output of the optimized model includes the maximum number of correct matching point pairs. 2. To address the issue of poor reconstruction model accuracy inherent in existing incremental Structure from Motion (SFM) sparse reconstruction algorithms, this paper proposes an enhanced incremental SFM sparse reconstruction algorithm. The study employs a similarity measurement algorithm to conduct an initial screening of the original matched views, selecting the top 70% as the standard for threshold setting and filtering, thereby generating preliminary matched views. Subsequently, the preliminary matched views are optimized based on temporal continuity to mitigate the impact of similar structures across time periods on the quantity and accuracy of the reconstructed model's point cloud. This process yields more complete and accurate matched views, resulting in a greater number of point clouds and a more precise sparse reconstruction outcome. 3. This article further integrates the incremental Structure from Motion (SfM) algorithm and enhanced feature matching with the Patch-Based Multi-View Stereo (PMVS) algorithm to generate more comprehensive three-dimensional models, thereby addressing the problem of incomplete reconstruction models found in existing dense reconstruction algorithms. This paper systematically reviews current solutions to these issues in Section 2. Section 3 provides a detailed introduction to the proposed improved algorithm.