Abstract. We present a novel method for estimation of articulated motion in depth scans. The method is based on a framework for regularization of vector-and matrix-valued functions on parametric surfaces.We extend augmented-Lagrangian total variation regularization to smooth rigid motion cues on the scanned 3D surface obtained from a range scanner. We demonstrate the resulting smoothed motion maps to be a powerful tool in articulated scene understanding, providing a basis for rigid parts segmentation, with little prior assumptions on the scene, despite the noisy depth measurements that often appear in commodity depth scanners.