Advanced 3D Horse Reconstruction: Integrating Image-to-3D Modelling and Non-Rigid Registration for On-Barn Body Measurements.

Precision livestock farming (PLF) relies on high-precision three-dimensional (3D) horse reconstruction and automatic body measurement to support refined breeding management and health surveillance. However, data collection is restricted by building environment noise and hardware layout constraints; complex equine body shapes and large individual variations induce local geometric distortions in reconstructed models, limiting field deployment. Drawing on generative 3D reconstruction, this study develops the first image-to-3D pipeline that leverages three consumer depth cameras to reconstruct high-fidelity 3D horse models, integrating reconstruction, non-rigid optimisation and automatic body measurement. The image-to-3D module accurately extracts core morphological traits such as torso outlines and limb ratios for initial reconstruction. To eliminate local geometric deformation and recover scene scale, coarse-to-fine non-rigid fitting optimisation with dynamic surface feature matching weights is proposed, strengthening alignment between reconstructed meshes and real horse anatomical structures. Comparative experiments on multiple equine datasets verify that our method surpasses existing algorithms in measurement precision and reconstruction integrity. Compared with baseline methods, the non-rigid registration reduces core body measurement errors and Chamfer Distance (CD) by over 50%, while increasing the F-Score by more than 20%. This work enables automatic horse body phenotyping and offers technical references for image-to-3D dimensional measurement of other livestock species in large-scale precise breeding.