In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D Gaussian Splatting stands out for its explicit representation of scenes using 3D Gaussians, making it appealing for tasks like 3D point cloud extraction and surface reconstruction. Motivated by its potential, we address the domain of 3D scene reconstruction, aiming to leverage the capabilities of the Microsoft HoloLens 2 for instant 3D Gaussian Splatting. We present HoloGS, a novel workflow utilizing HoloLens sensor data, which bypasses the need for pre-processing steps like Structure from Motion by instantly accessing the required input data i.e. the images, camera poses and the point cloud from depth sensing. We provide comprehensive investigations, including the training process and the rendering quality, assessed through the Peak Signal-to-Noise Ratio, and the geometric 3D accuracy of the densified point cloud from Gaussian centers, measured by Chamfer Distance. We evaluate our approach on two self-captured scenes: An outdoor scene of a cultural heritage statue and an indoor scene of a fine-structured plant. Our results show that the HoloLens data, including RGB images, corresponding camera poses, and depth sensing based point clouds to initialize the Gaussians, are suitable as input for 3D Gaussian Splatting.

翻译：在摄影测量学、计算机视觉与计算机图形学领域，神经三维场景重建任务催生了多种技术探索。其中，三维高斯泼溅凭借其利用三维高斯体对场景进行显式表示的特性脱颖而出，在三维点云提取与表面重建等任务中展现出独特优势。受其潜力驱动，本研究聚焦三维场景重建领域，旨在利用微软HoloLens 2的即时三维高斯泼溅能力。我们提出HoloGS——一种基于HoloLens传感器数据的新型工作流，通过即时获取深度传感提供的图像、相机位姿与点云等所需输入数据，绕开了运动恢复结构等预处理步骤。我们开展了系统性研究，涵盖训练过程与渲染质量（通过峰值信噪比评估），以及高斯体中心稠密化点云的几何三维精度（以倒角距离衡量）。在两个自采场景（户外文化遗产雕像场景与室内精细结构植物场景）上的评估结果表明：包含RGB图像、对应相机位姿及基于深度传感点云（用于初始化高斯体）的HoloLens数据，完全适用于三维高斯泼溅的输入。