# Sparse Indoor Camera Positioning with Fiducial Markers
## 2 Related Works  
Estimating the pose of a set of sparse cameras under the same coordinate system is a fundamental task in computer vision applications such as 3D reconstruction, robotics, and augmented reality. Prior methods often rely on overlapping fields of view between cameras to establish correspondences and compute relative poses [4; 5; 6]. However, in real-world scenarios, cameras may be positioned with non-overlapping views, implying a cost reduction by minimizing the number of cameras required, presenting unique challenges for accurate pose estimation. Recent developments have introduced various methods for general indoor positioning [20; 21; 22]. However, these techniques are not able to address the pose estimation of fixed sparse cameras, highlighting the importance of image-based solutions for tackling this problem. In non-overlapping camera networks, the absence of shared visual information complicates finding correspondences between camera views, which is essential for conventional pose estimation techniques. Despite significant advancements, as highlighted in previous studies [6; 23; 24], achieving reliable pose estimation in large-scale environments without overlapping views remains an unresolved challenge. Each of these methods has advanced the field, yet they continue to rely on environments that provide substantial visual texture, indicating that this remains a critical area for ongoing research, particularly in large scenarios where such details are scarce. In scenarios requiring precise camera positioning over large distances, total stations and laser measurement tools provide an alternative approach, offering high accuracy but at increased costs and operational complexity. These instruments are particularly advantageous in scenarios where traditional methods face challenges in precision or scale. However, the exploration of camera positioning using these methodologies remains underdeveloped, especially for multi-camera pose estimation in sparse environments. While some studies have examined their application in camera pose estimation [25; 26], further research is essential to fully integrate high-precision measurement tools into practical and scalable solutions for complex indoor environments. Various methodologies, prominently feature-based and marker-based, have been developed to tackle camera pose estimation. Feature-based systems, effective in conditions with sufficient overlap, detect and match key points across images to compute relative camera poses. However, in non-overlapping camera networks, the absence of shared features poses challenges, as traditional feature-based approaches cannot establish the necessary correspondences for pose estimation. Structure from Motion (SfM) is a computational technique initially detailed by [PERSON] [27], which reconstructs 3D structures through the analysis of motion sequences captured from multiple viewpoints. Essential tools such as OpenDroneMap [28], Pix4D [29], and COLMAP [30] facilitate this process by using feature-matching techniques across overlapping images to estimate camera poses and construct 3D models. A significant hurdle in SfM is the lack of common features between images, which is critical for ensuring accurate view alignment and thus affects the overall efficacy of the reconstruction process, making its application challenging in indoor environments or in scenarios without overlapping views between the cameras capturing these images. While deep learning has broadened the scope of Structure from Motion (SfM), challenges continue in scenarios with non-overlapping views, despite the advances such as those by [PERSON] et al. [14] and [PERSON] et al. [15]. Gaussian splatting, developed by [PERSON] et al. [31], marks a significant evolution in traditional SfM methods by utilizingneural networks to model scenes as continuous volumetric functions. This technique significantly improves the handling of complex scenes and smoothens the reconstruction process. Tools from PolyCam [32] support the rapid testing and deployment of Gaussian splatting.