In this paper, we present an efficient 3D shape rejection algorithm for unlabeled 3D markers. The problem is important in domains such as rehabilitation and the performing arts. There are three key innovations in our approach - (a) a multi-resolution shape representation using Haar wavelets for unlabeled markers, (b) a multi-resolution shape metric and (c) a shape rejection algorithm that is predicated on the simple idea that we do not need to compute the entire distance to conclude that two shapes are dissimilar. We tested the approach on a real-world pose classification problem with excellent results. We achieved a classification accuracy of 98% with an order of magnitude improvement in terms of computational complexity over a baseline shape matching algorithm.