# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 6 Experimental Results  
The experimental results demonstrate the process of obtaining a 3D human model from NIR-SPI imaging at distances of 0.5 m and 1 m from the NIR-SPI camera under night-time illumination conditions using the methods proposed in Section 5 (see Figure 4). This process involves several key components that ensure accurate pose estimation and 3D reconstruction (see Figure 7):  
Figure 6: Sensitivity and robustness analysis of reconstruction error under parameter perturbations: (**a**) illustrates the impact of single-parameter variations (\(\pm 20\%\)) on reconstruction error, highlighting the high sensitivity of pose (\(\theta\)) perturbations compared to rotation (\(R\)), body shape (\(\beta\)), and translation (\(t\)), (**b**) heatmap presents the correlation matrix from a Monte Carlo analysis, showing the relationships between perturbations and reconstruction error, with pose (\(\theta\)) exhibiting the strongest correlation.  
Figure 7: The environment scenario testing framework simulates real-world conditions for image capture using a single-pixel camera (SPC). The setup includes a background lighting system to control illumination.  
1. NIR-SPI Imaging System: * The NIR-SPI camera captures depth-encoded images using near-infrared illumination, allowing for reliable imaging under low-light conditions. * The camera is positioned at a fixed location to maintain consistent capture angles across different experiments. The subject was placed at predefined distances (0.5 m and 1.0 m) using floor markers to maintain accuracy between trials. * The field of view (\(74^{\circ}\times 57^{\circ}\)) is selected to cover human movements within the experimental range.
2. Illumination control and environmental isolation: * A background lighting system is integrated to simulate controlled night-time conditions, reducing the effects of ambient light interference. * This system ensures that the near-infrared (NIR) signals remain dominant, improving the accuracy of the depth estimation. * The experiments were conducted in a light-controlled chamber to prevent external light leakage that could affect image capture. The walls and floor materials were selected to minimize reflections and absorb stray light. * The clothing and background materials of the human subject were standardized to reduce variations in light absorption and scattering.
3. Deep Learning Models and Data Processing: * The approach leverages deep learning models proposed for the LWIRPOSE dataset [60], ensuring high accuracy in pose estimation. * The calibration of the vision system is performed using established methods [72] the alignment between the depth maps and the NIR-SPI images. * The experimental data are collected in a controlled laboratory setting, where the performance of the vision system is evaluated (Figure 7).
4. Evaluation metrics for reconstruction quality [70]: * Mean Vertex-to-Vertex Distance (V2V): Measures the accuracy of surface reconstruction (lower values indicate better performance). * Mean Per-Joint Position Error (MPJPE): Evaluates pose accuracy by comparing predicted joint positions with ground truth (lower values indicate better performance). * These metrics provide a comprehensive evaluation of the effectiveness of the self-supervised learning model and the robustness of the proposed methodology (Table 3).