Abstract—Object parsing and segmentation from point clouds are challenging tasks because the relevant data is available only as thin structures along object boundaries or other features, and is corrupted by large amounts of noise. To handle this kind of data, flexible shape models are desired that can accurately follow the object boundaries. Popular models such as Active Shape and Active Appearance models lack the necessary flexibility for this task, while recent approaches such as the Recursive Compositional Models make model simplifications in order to obtain computational guarantees. This paper investigates a hierarchical Bayesian model of shape and appearance in a generative setting. The input data is explained by an object parsing layer, which is a deformation of a hidden PCA shape model with Gaussian prior. The paper also introduces a novel efficient inference algorithm that uses informed data-driven proposals to initialize local searches for the hidden variables. Applied to the problem of object parsing from structured point clouds such as edge detection images, the proposed approach obtains state of the art parsing errors on two standard datasets without using any intensity information. The Active Shape  and Active Appearance Models  use a simplified object representation using Principal Component Analysis (PCA) and use local information to search for a solution. The Active Shape Models (ASM) contain only a PCA shape model and alternate one step that searches for local boundary evidence on the shape normals with another step that reprojects the evidence onto the PCA hyperplane, until convergence. The tradeoff made by the ASM is the local search for the solution, based on partial image information existent on the shape normals. Because of this trade-off, the result depends on initialization. Index Terms—object parsing, hierarchical models, MRF optimization, active shape model.
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