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In this paper, we show how the Metropolis-Hastings algorithm can be used to sample shapes from a distribution defined over the space of signed distance functions. We extend the basic random walk Metropolis-Hastings method to high-dimensional curves using a proposal distribution that can simultaneously maintain the signed distance function property and the ergodic requirement. We show that detailed balance is approximately satisfied and that the Markov chain will asymptotically converge. A keydoi:10.1109/iccvw.2009.5457687 dblp:conf/iccvw/ChenR09 fatcat:6aui5vwdnvayrap337yo7tsesa