The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
FlowNet3D: Learning Scene Flow in 3D Point Clouds
Open accessAbstract
Many applications in robotics and human-computer interaction can benefit from understanding 3D motion of points in a dynamic environment, widely noted as scene flow. While most previous methods focus on stereo and RGB-D images as input, few try to estimate scene flow directly from point clouds. In this work, we propose a novel deep neural network named FlowNet3D that learns scene flow from point clouds in an end-to-end fashion. Our network simultaneously learns deep hierarchical features of point clouds and flow embeddings that represent point motions, supported by two newly proposed learning layers for point sets. We evaluate the network on both challenging synthetic data from FlyingThings3D and real Lidar scans from KITTI. Trained on synthetic data only, our network successfully generalizes to real scans, outperforming various baselines and showing competitive results to the prior art. We also demonstrate two applications of our scene flow output (scan registration and motion segmentation) to show its potential wide use cases.
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Cites
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles, R. Q., Su, H., Kaichun, M., & Guibas, L. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.16 · Cited by 9,237
Self-citationDynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
Flownet: Learning optical flow with convolutional networks
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FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
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ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · arXiv · doi:10.48550/arxiv.1512.03012 · Cited by 2,330
Self-citationObject scene flow for autonomous vehicles
Menze, M., & Geiger, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298925 · Cited by 2,270
ShapeNet: An Information-Rich 3D Model Repository
Chang, A. X., Funkhouser, T. A., Guibas, L. J., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2023) · doi:10.5281/zenodo.10089705 · Cited by 1,623
Self-citationUnique Signatures of Histograms for Local Surface Description
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Argoverse: 3D Tracking and Forecasting With Rich Maps
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Offboard 3D Object Detection from Point Cloud Sequences
Qi, C. R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., & Anguelov, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00607 · Cited by 176
Self-citationHeadGAN: One-shot Neural Head Synthesis and Editing
Doukas, M. C., Zafeiriou, S., & Sharmanska, V. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01413 · Cited by 117
PointRNN: Point Recurrent Neural Network for Moving Point Cloud Processing
Fan, H., & Yang, Y. (2019) · arXiv · doi:10.48550/arxiv.1910.08287 · Cited by 80
Fu, Y., Lei, Y., Wang, T., Patel, P., Jani, A. B., Mao, H., Curran, W. J., Liu, T., & Yang, X. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101845 · Cited by 75
Metadata
- Authors
- Liu, X., Qi, C. R., & Guibas, L. J.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 529-537
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962771259
- Cited by
- 499
- References
- 32
- FWCI
- 20.58
- Type
- conference paper
- Language
- en
- Open access
- green
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