Abstract
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.
Abstract (translated)
脊柱角度是衡量身体平衡的重要指标。恢复人体的三维形状并估计脊椎中心线非常重要。现有的基于多图像的身体重建方法需要昂贵的设备和复杂的程序，而基于单张图像的方法则由于遮挡和视角限制难以准确估算如脊椎中心线等内部结构。本研究提出了一种补偿多图像法缺点及解决单一图像法局限性的方法。我们设计了一个集成了四个方向深度图的三维人体姿态分析系统，用于恢复一个三维的人体模型并自动估计脊柱中心线。通过全局和精细注册的分层匹配，可以应对噪声和遮挡的影响进行重建。此外，应用自适应顶点减少以保持网格的分辨率和形状可靠性，并利用细节层次（LoD）集成同时保证了脊椎角度估算的准确性和稳定性。所提出的方法能够在不依赖训练数据或复杂神经网络模型的情况下实现高精度的三维脊柱注册估计，并且验证确认匹配质量得到了改善。
URL
https://arxiv.org/abs/2512.12718
