An accessible and intuitive approach to 3D data modeling for morphometric surface evaluation in pig farming

• Dense 3D point clouds can be reduced to a small number of reference points. • Deliberate arrangement of reference points enables precise surface reconstruction. • The pig population can be described by an average shape and variations from it. • Shape attributes can be specified as size, form, posture and data noise. The recording of pig surfaces allows for the estimation of growth-related body size dimensions from which the animals’ condition can be inferred. Furthermore, for purposes such as animal breeding, it is of particular interest to describe the shape variations within the population. Therefore, we built a Statistical Shape Model (SSM) that enables a quantitative and visual exploration of variation in size, form and posture in an intuitive way. We included 4315 images from 582 animals with a body weight ranging from 50 to 140 kg. After data processing, i.e. the removal of data points corresponding to the head and tail, the 3D point clouds were reduced to an equal number of reference points. These reference points were arranged so that the pig surface could be reconstructed via Bézier surface patches. Then, Procrustes Analysis was performed to align the reference points from all shapes. A mean shape and its main modes of variation were derived, some of which were used to correct the posture of the animals. A linear regression for weight prediction resulted in a mean absolute error of 5.06 kg before and 2.84 kg after data processing. The median error of surface reconstruction was 2.5 mm taking eight surface patches with 45 reference points and 1.3 mm taking 32 surface patches with 153 reference points into account. The first six modes of variation from the SSM indicated differences in size and posture. Interestingly, regions prone to noise were also revealed. In conclusion, we present a novel approach that can be used to receive a compact number of corresponding data points, providing data that is easier and more efficient to handle while exposing shape-specific attributes. Our approach is helpful to visualize and compare shapes, including the collection of body size measurements as well as unsupervised and supervised clustering for more objective data-driven decision-making.