The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
3DRegNet: A Deep Neural Network for 3D Point Registration
Open accessAbstract
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.
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Cites
Deep Residual Learning for Image Recognition
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Adam: A Method for Stochastic Optimization
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A method for registration of 3-D shapes
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PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
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“Taking inspiration from [4, 37, 42], we propose the use of Curriculum Learning (CL) data augmentation.”
MethodKinectFusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Fitzgibbon, A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohi, P., Shotton, J., & Hodges, S. (2011) · 2011 10th IEEE International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2011.6092378 · Cited by 4,008
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Fast Point Feature Histograms (FPFH) for 3D registration
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PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud
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Cited by
GeoTransformer: Fast and Robust Point Cloud Registration With Geometric Transformer
Qin, Z., Yu, H., Wang, C., Guo, Y., Peng, Y., Ilic, S., Hu, D., & Xu, K. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3259038 · Cited by 279
Algebraically rigorous quaternion framework for the neural network pose estimation problem
Lin, C., Hanson, A. J., & Hanson, S. M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01296 · Cited by 9
Metadata
- Authors
- Pais, G. D., Ramalingam, S., Govindu, V. M., Nascimento, J. C., Chellappa, R., & Miraldo, P.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 7191-7201
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2934857476
- Cited by
- 268
- References
- 66
- FWCI
- 34.30
- Type
- conference paper
- Language
- en
- Open access
- green
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- Pais, G. D., Ramalingam, S., Govindu, V. M., Nascimento, J. C., Chellappa, R., & Miraldo, P. (2020). 3DRegNet: A Deep Neural Network for 3D Point Registration. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00722
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