Semantic Keypoint Extraction for Scanned Animals Using Multi-Depth-Camera Systems

Keypoint annotation in pointclouds is an impor- tant task for 3D reconstruction, object tracking and alignment, in particular in deformable or moving scenes. In the context of agriculture robotics, it is a critical task for livestock au- tomation to work toward condition assessment or behaviour recognition. In this work, we propose a novel approach for semantic keypoint annotation in pointclouds, by reformulating the keypoint extraction as a regression problem of the distance between the keypoints and the rest of the pointcloud. We use the distance on the pointcloud manifold mapped into a radial basis function (RBF), which is then learned using an encoder-decoder architecture. Special consideration is given to the data augmentation specific to multi-depth-camera systems by considering noise over the extrinsic calibration and camera frame dropout. Additionally, we investigate computationally efficient non-rigid deformation methods that can be applied to animal pointclouds. Our method is tested on data collected in the field, on moving beef cattle, with a calibrated system of multiple hardware-synchronised RGB-D cameras.