The spinal angle is an important indicator of body balance. It is important to restore the 3D shape of the human body and estimate the spine center line. Existing mul-ti-image-based body restoration methods require expensive equipment and complex pro-cedures, and single image-based body restoration methods have limitations in that it is difficult to accurately estimate the internal structure such as the spine center line due to occlusion and viewpoint limitation. This study proposes a method to compensate for the shortcomings of the multi-image-based method and to solve the limitations of the sin-gle-image method. We propose a 3D body posture analysis system that integrates depth images from four directions to restore a 3D human model and automatically estimate the spine center line. Through hierarchical matching of global and fine registration, restora-tion to noise and occlusion is performed. Also, the Adaptive Vertex Reduction is applied to maintain the resolution and shape reliability of the mesh, and the accuracy and stabil-ity of spinal angle estimation are simultaneously secured by using the Level of Detail en-semble. The proposed method achieves high-precision 3D spine registration estimation without relying on training data or complex neural network models, and the verification confirms the improvement of matching quality.

翻译：脊柱角度是评估身体平衡的重要指标。准确重建人体三维形态并估计脊柱中心线至关重要。现有基于多图像的人体重建方法需要昂贵设备与复杂流程，而基于单图像的重建方法因遮挡与视角限制，难以准确估计脊柱中心线等内部结构。本研究提出一种融合多图像方法优势并改进单图像方法局限性的解决方案：通过集成四个方向的深度图像重建三维人体模型，构建可自动估计脊柱中心线的三维体态分析系统。通过全局配准与精细配准的分层匹配，实现对噪声与遮挡的鲁棒重建。应用自适应顶点缩减算法保持网格分辨率与形状可信度，并借助细节层次集成技术同步保障脊柱角度估计的精度与稳定性。该方法无需依赖训练数据或复杂神经网络模型即可实现高精度三维脊柱配准估计，验证实验证实了其匹配质量的显著提升。