Matches in SemOpenAlex for { <https://semopenalex.org/work/W2934857476> ?p ?o ?g. }
- W2934857476 abstract "We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available." @default.
- W2934857476 created "2019-04-11" @default.
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- W2934857476 date "2019-04-02" @default.
- W2934857476 modified "2023-09-23" @default.
- W2934857476 title "3DRegNet: A Deep Neural Network for 3D Point Registration" @default.
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