Sim2real Cattle Joint Estimation in 3D point clouds

Understanding the well-being of cattle is crucial in various agricultural contexts. Cattle's body shape and joint articulation carry significant information about their welfare, yet acquiring comprehensive datasets for 3D body pose estimation presents a formidable challenge. This study delves into the construction of such a dataset specifically tailored for cattle. Leveraging the expertise of digital artists, we use a single animated 3D model to represent diverse cattle postures. To address the disparity between virtual and real-world data, we augment the 3D model's shape to encompass a range of potential body appearances, thereby narrowing the "sim2real" gap. We use these annotated models to train a deep-learning framework capable of estimating internal joints solely based on external surface curvature. Our contribution is specifically the use of geodesic distance over the surface manifold, coupled with multilateration to extract joints in a semantic keypoint detection encoder-decoder architecture. We demonstrate the robustness of joint extraction by comparing the link lengths extracted on real cattle mobbing and walking within a race. Furthermore, inspired by the established allometric relationship between bone length and the overall height of mammals, we utilise the estimated joints to predict hip height within a real cattle dataset, extending the utility of our approach to offer insights into improving cattle monitoring practices.