# SIFT-Based Depth Estimation for Accurate 3D Reconstruction in Cultural Heritage Preservation
## 2 Materials and Methods
### Recent Works on 3D Reconstruction
This technique demonstrates efficiency when each stereo pair image has an overlapping area of 70-80% consecutively throughout the image shots. Thus, each image shot should be taken within 15-20 degrees. Therefore, taking a picture of a cultural heritage object to generate a 3D model was not easy for the Thai artist. Figure 2a,b shows stereo pair image matching using the SIFT technique. It should be noted that input images were taken outside the overlapping range (\(\geq\)80%), with the corresponding degree of each image being greater than 20 degrees. Therefore, sufficient stereo pair image matching cannot be generated to create a 3D model. Figure 2c shows the 3D reconstruction of multiple 2D images using the stereo pair image matching technique. Figure 1: 3D mesh model generated by the photogrammetry method. Moreover, to solve the problem of the stereo pair matching point cloud, a more sophisticated technique called Oriented FAST and Rotated BRIEF (ORB) has been introduced [40; 41; 42]. This technique is robust against geometric transformation when the camera is rotating around the object, and it also provides the capability of lens distortion correction and finding the object orientation in the image. However, this technique requires more computational time, and the user must consider whether they want to capture a huge object despite it taking more time to process. After solving the orientation and depth computation of images, a 3D model can be constructed using the point cloud and stereo-dense matching according to the results of computation. Figure 3 shows the results of 3D reconstruction using the dense matching of the point cloud. In this study, traditional 3D preservation methods (Table 1) are compared with recent advancements in 3D reconstruction using 2D images. This paper proposes 3D reconstruction techniques for the digital preservation of cultural heritage objects based on depth estimation using SIFT. The contribution of this work consists of (1) image registration, which plays a fundamental role meticulously aligning multiple images with overlapping sections to establish spatial relationships; (2) depth computation, for determining the distance between objects and the camera, is crucial for accurate 3D modeling; (3) dense matching algorithms, which establish correspondences among points in different images following depth estimation, which, when combined with point cloud data, enables detailed 3D object reconstruction; and (4) the SIFT algorithm, which is essential in this process, for locating distinctive and invariant image features to facilitate image registration and depth calculation. The challenge of the proposed method among the recent works in the field  
Figure 2: Stereo pair image matching using SIFT [37]. of 3D reconstruction of tangible cultural heritage using 2D images (SfM and MVS) is the computational cost of depth estimation associated with SIFT-based 3D reconstruction. SIFT-based depth estimation, while computationally efficient, requires certain limitations to be considered. Due to its focus on distinctive keypoints, SIFT can generate relatively sparse point clouds compared to dense techniques like MVS, potentially limiting the attainable resolution of the final 3D model. Moreover, SIFT feature matching can be sensitive to variations in lighting, viewpoint, and texture, sometimes introducing inaccuracies in depth estimation, especially for scenes with fewer distinct features. In objects possessing highly complex geometries, SIFT might struggle to reliably establish sufficient keypoints, hindering the capture of intricate details. When compared to SfM and MVS, SIFT offers the advantage of lower computational cost due to its keypoint-centric approach. Conversely, SfM can incur higher computational demands, particularly during bundle adjustment, while MVS is often the most computationally intensive due to its dense reconstruction focus.