Marker-based stereophotogrammetry (MB) is the gold standard for gait analysis, but its clinical use is limited due to high costs, long set-up times, and patient discomfort. Recent advances in depth sensing and machine learning have made markerless (ML) gait analysis a promising alternative for clinical applications, especially when ease of setup is essential. The development of RGB-Depth technology further enhances ML capabilities by integrating color and depth data, thus allowing for a color point cloud reconstruction. In the literature, various single-camera ML algorithms have been proposed, including deterministic and deep learning-based approaches. However, these algorithms rely on 2D video analysis, requiring manual identification of anatomical landmarks and failing to capture out-of-plane movement.