# SIFT-Based Depth Estimation for Accurate 3D Reconstruction in Cultural Heritage Preservation
## 2 Materials and Methods  
### Recent Works on 3D Reconstruction  
Recent works relating to the 3D reconstruction of tangible cultural heritage using 2D images have been investigated. Various techniques exist for the 3D reconstruction of cultural heritage artifacts using 2D images. Traditional passive methods, such as employing Structure from Motion (SfM) and multi-view stereo (MVS), are well-established and rely on feature extraction and matching across multiple images to generate sparse or dense point clouds. SfM is a powerful technique used to reconstruct the 3D structure of a scene and camera parameters from a collection of images or videos. By analyzing how features within images shift across multiple viewpoints, SfM algorithms determine camera positions and motion trajectories. This information is then used to generate a 3D point cloud representing the scene within a spatial coordinate system. There are several SfM approaches, including incremental, distributed, and global, each differing in how they initialize and refine parameter estimations [19; 20; 21]. While SfM excels at determining camera positions and the general 3D layout of a scene, its reliance on sparse feature matching can lead to incomplete reconstruction. To address this, MVS techniques are used to significantly enhance 3D models. MVS leverages the camera pose information provided by SfM to gain a richer understanding of the scene. By employing image rectification and advanced stereo-matching techniques, MVS identifies corresponding points across multiple images with greater accuracy. This results in a denser point cloud and a substantially improved 3D reconstruction. Common MVS methods include voxel-based reconstruction, feature propagation, and depth map fusion [22]. Recent advancements include image processing approaches that utilize kernel-based filtering and shape repair algorithms to transform 2D images into 3D representations [23]. Furthermore, the application of low-cost RGB-D sensors, like the Microsoft Kinect and Intel RealSense camera F200, has shown promise in cultural heritage preservation projects such as the documentation of Brazilian Baroque sculptures [24]. Additionally, close-range photogrammetry techniques, exemplified by the A-KAZE algorithm applied to temples in Yogyakarta, offer detailed 3D reconstruction from 2D photographs [25]. Beyond geometric reconstruction, semantic modeling approaches aim to extract not only spatial data but also historical and contextual information from cultural heritage structures using frameworks like CIDOC CRM [26]. This allows for the creation of knowledge representation graphs, enriching the understanding of these valuable artifacts. Recent methods for 3D reconstruction in practice include:  
1. 3D reconstruction using dense image matching: This technique is mostly used for the 3D reconstruction of buildings and architecture from aerial images. The aerial images tend to provide only the detail of the top view of an object. However, by using the dense map, this method can compute differences in object intensity of the image in the form of shadow, shading, and reflection of the object. The algorithm can compute the dimensions of the object by using the measurement of these properties [17; 18; 27; 28; 29; 30; 31]. 2. 3D reconstruction using the photogrammetry method: Commercial software, e.g., AgiSoft, Reality Capture, and ReCap, uses the point cloud as the extracted feature to generate the 3D model. This software commonly requires the images taken to have an overlap, conveyed throughout the series of images to build the 3D model using the overlapping relation. Figure 1 shows the mesh model of the [PERSON] generated from the point cloud [32; 33; 34; 35; 36]. One major concern with 3D reconstruction from multiple 2D images is stereo pair image matching. The earlier stage of corresponding point cloud matching was introduced by Scale Invariant Feature Transform (SIFT) [37], Speeded Up Robust Features (SURF) [38], and Maximal Stable Extreme Region [39].