Abstract
The practical use of the latest methods for supervised 3D shape co-segmentation is limited by the requirement of diverse training data and a watertight mesh representation. Driven by practical considerations, we assume only one reference shape to be available and the query shape to be provided as a partially visible point cloud. We propose a novel co-segmentation approach that constructs a part-based object representation comprised of shape appearance models of individual parts and isometric spatial relations between the parts. The partial query shape is pre-segmented using planar cuts, and the segments accompanied by the learned representation induce a compact Conditional Random Field (CRF). CRF inference is performed efficiently by \(A^*\)-search with global optimality guarantees. A comparative evaluation with two baselines on partial views generated from the Labelled Princeton Segmentation Benchmark and point clouds recorded with an RGB-D sensor demonstrate superiority of the proposed approach both in accuracy and efficiency.
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Reported as one minus Rand index, by convention.
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This work was supported by German Research Foundation (DFG) under grant BE 2556/12 ALROMA in priority programme SPP 1527 Autonomous Learning.
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Araslanov, N., Koo, S., Gall, J., Behnke, S. (2016). Efficient Single-View 3D Co-segmentation Using Shape Similarity and Spatial Part Relations. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_24
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DOI: https://doi.org/10.1007/978-3-319-45886-1_24
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