VirtualButcher: Coarse-to-fine Annotation Transfer of Cutting Lines on Noisy Point Cloud Reconstruction

Abstract

Robotics and automation are rapidly becoming part of meat processing operations. Current automation of breaking down a carcass into primals relies on guidance from X-ray, inter-connected with robotised band-saws. While yielding very accurate cutting lines, the use of vision systems for guidance would be significantly more affordable. This work proposes a novel method that solves the annotation transfer between a 3D noise-free cut-ting line annotated on a CT acquired canonical model and a noisy target in the form of a point cloud acquired by RGB-D cameras. The proposed coarse-to-fine method initially aligns the posture of each body using a non-rigid deformation algorithm and then performs a local search to solve the surface correspondence which is later used to morph the template non-rigidly. We quantitatively assess the approach by benchmarking with multiple state-of-the-art algorithms on a public available human pose dataset. We also present a proof of concept evaluation on lamb carcasses.