Enhancing 3D Scan Quality and Automating Data Processing for Equine Back Analysis in Saddle Fitting Applications

Abstract

Poor saddle fit can cause pain, muscle atrophy, and reduced performance in horses, while back shape changes over time make regular reassessment essential. To address this, Voltaire Group developed a mobile app using iPhone 3D scanning, but the current system struggles with scan accuracy, robustness, and extraction of meaningful geometric features. This research improves the reconstruction and analysis pipeline to enable more precise and reliable saddle fitting. The original workflow relied on surfel-based reconstruction followed by Poisson meshing, which often produced imprecision and inconsistencies due to low-resolution depth data. In contrast, the proposed workflow filters the raw depth data and applies a volumetric Truncated Signed Distance Function (TSDF) representation combined with marching cubes meshing. Comparative analyses were conducted between methods, with evaluation metrics focused on reconstruction precision relative to reference scans. In addition, a novel technique for detecting the dominant symmetry plane—tailored to horse back geometry—was introduced, along with newgeometric measurements relevant to saddle fit. Results show that TSDF-based reconstruction increases precision by up to 30% while supporting real-time processing. The new pipeline also avoids the error accumulation observed in the previous point cloud–to–mesh workflow, ensuring consistent and reliable outcomes across configurations. The symmetry plane detection method proved highly effective, reducing orientation errors by an average of 2 cm. Overall, this work contributes a robust and precise 3D reconstruction pipeline for equine back scanning, supporting improved saddle fitting practices and demonstrating the potential of mobile 3D scanning in equestrian applications.