swMATH ID: 32560
Software Authors: Haowen Deng, Tolga Birdal, Slobodan Ilic
Description: PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors. We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the folding-based auto-encoding of well known point pair features, PPF-FoldNet offers many desirable properties: it necessitates neither supervision, nor a sensitive local reference frame, benefits from point-set sparsity, is end-to-end, fast, and can extract powerful rotation invariant descriptors. Thanks to a novel feature visualization, its evolution can be monitored to provide interpretable insights. Our extensive experiments demonstrate that despite having six degree-of-freedom invariance and lack of training labels, our network achieves state of the art results in standard benchmark datasets and outperforms its competitors when rotations and varying point densities are present. PPF-FoldNet achieves 9
Homepage: https://arxiv.org/abs/1808.10322
Keywords: Computer Vision; Pattern Recognition; arXiv_cs.CV; Computational Geometry; arXiv_cs.CG; Machine Learning; arXiv_cs.LG; Robotics; arXiv_cs.RO
Related Software: BundleFusion; 3DFeat-Net; FoldingNet; 3DMatch; PPFNet; PointNet; TensorFlow
Cited in: 1 Publication

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