用四视角2D深度图实现无需训练的3D人体姿态分析，精准估计脊柱中线。
Spinal Line Detection for Posture Evaluation through Train-ing-free 3D Human Body Reconstruction with 2D Depth Images
- 融合四个方向深度图，通过分层匹配恢复3D人体模型。
- 在噪声和遮挡下仍能保持高精度脊柱中线估计，误差小。
- 适合医疗评估场景，无需神经网络或大量训练数据。
脊柱角度是身体平衡的重要指标，准确重建人体3D形态并估计脊柱中线至关重要。现有多视角人体重建方法依赖昂贵设备与复杂流程，单视角方法则受限于遮挡与视角问题，难以精确估计内部结构如脊柱中线。本文提出一种无需训练数据的3D人体姿态分析系统，利用四视角2D深度图像重建3D人体模型，并自动提取脊柱中心线。通过全局与细粒度分层匹配，有效应对噪声与遮挡；采用自适应顶点简化保持网格分辨率与形状可靠性；结合层级细节集成（Level of Detail ensemble）提升脊柱角度估计的精度与稳定性。实验验证了该方法在无训练数据和复杂神经网络的前提下，实现了高精度3D脊柱注册估计，显著提升了匹配质量。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。