Due to the increasing application demands, lightweight device-based 3D recovery draws many attentions from a wide group of researchers in both academic and industrial fields. The current 3D reconstruction solutions are commonly achieved either using depth data or RGB data. The depth data usually come from a deliberately designed hardware for specific tasks, while the RGB data-based solutions only employ a single RGB camera with vision-based computing algorithms. Limitations are expected from both. Depth sensors are commonly either bulky or relatively expensive compared to RGB cameras, thus of less flexibility. Normal RGB cameras usually have better mobility but less accuracy in 3D sensing than depth sensors. Recently, machine learning based depth estimation has also been presented. However, its accuracy is still limited. To improve the flexibility of the 3D reconstruction system without loss in accuracy, this paper presents a solution of unconstrained Metrically Accurate 3D Reconstruction System (MARS) for 3D sensing based on a consumer-grade camera. With a simple initialization from a depth map, the system can achieve incremental 3D reconstruction with a stable metric scale. Experiments are conducted using both real-world data and public datasets. Competitive results are obtained using the proposed system compared with several existing methods.

由于不断增长的应用需求，基于轻型设备的3D恢复引起了学术界和工业领域众多研究人员的广泛关注。当前的3D重建解决方案通常使用深度数据或RGB数据来实现。深度数据通常来自用于特定任务的经过精心设计的硬件，而基于RGB数据的解决方案仅使用具有基于视觉计算算法的单个RGB摄像机。两者都有局限性。深度传感器通常比RGB相机笨重或相对昂贵，因此灵活性较差。普通的RGB相机通常比深度传感器具有更好的移动性，但在3D感测中的准确性较低。最近，还提出了基于机器学习的深度估计。但是，其准确性仍然有限。为了提高3D重建系统的灵活性而又不损失准确性，本文提出了一种基于消费级相机的无约束，精确的3D重建3D重建系统（MARS）解决方案。通过深度图的简单初始化，系统可以以稳定的度量标准实现增量3D重建。使用真实数据和公共数据集进行实验。使用本发明的系统与几种现有方法相比可以获得竞争性结果。该系统可以以稳定的度量标准实现增量3D重建。使用真实数据和公共数据集进行实验。使用本发明的系统与几种现有方法相比可以获得竞争性结果。该系统可以以稳定的度量标准实现增量3D重建。使用真实数据和公共数据集进行实验。使用本发明的系统与几种现有方法相比可以获得竞争性结果。