The DOI matches its Crossref record; 83 of the 83 references it cites were found in a registry or an index.
Space-time Neural Irradiance Fields for Free-Viewpoint Video
Abstract
We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video. Our learned representation enables free-viewpoint rendering of the input video. Our method builds upon recent advances in implicit representations. Learning a spatiotemporal irradiance field from a single video poses significant challenges because the video contains only one observation of the scene at any point in time. The 3D geometry of a scene can be legitimately represented in numerous ways since varying geometry (motion) can be explained with varying appearance and vice versa. We address this ambiguity by constraining the time-varying geometry of our dynamic scene representation using the scene depth estimated from video depth estimation methods, aggregating contents from individual frames into a single global representation. We provide an extensive quantitative evaluation and demonstrate compelling free-viewpoint rendering results.
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Citations
Cites
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_24 · Cited by 5,351
KinectFusion: 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
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., & Lovegrove, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00025 · Cited by 3,422
A volumetric method for building complex models from range images
Curless, B., & Levoy, M. (1996) · Proceedings of the 23rd annual conference on Computer graphics and interactive techniques · doi:10.1145/237170.237269 · Cited by 3,124
Occupancy Networks: Learning 3D Reconstruction in Function Space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., & Geiger, A. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00459 · Cited by 2,708
KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., et al. (2011) · Proceedings of the 24th annual ACM symposium on User interface software and technology · doi:10.1145/2047196.2047270 · Cited by 2,110
A Naturalistic Open Source Movie for Optical Flow Evaluation
Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842
MVSNet: Depth Inference for Unstructured Multi-view Stereo
Yao, Y., Luo, Z., Li, S., Fang, T., & Quan, L. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_47 · Cited by 1,455
High-quality video view interpolation using a layered representation
Zitnick, C. L., Kang, S. B., Uyttendaele, M., Winder, S., & Szeliski, R. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015766 · Cited by 1,385
Cited by
Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction
Sun, C., Sun, M., & Chen, H.-T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00538 · Cited by 939
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
Reiser, C., Peng, S., Liao, Y., & Geiger, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01407 · Cited by 668
HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video
Weng, C.-Y., Curless, B., Srinivasan, P. P., Barron, J. T., & Kemelmacher-Shlizerman, I. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01573 · Cited by 452
Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Yang, Z., Gao, X., Zhou, W., Jiao, S., Zhang, Y., & Jin, X. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01922 · Cited by 452
HexPlane: A Fast Representation for Dynamic Scenes
Cao, A., & Johnson, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00021 · Cited by 435
Metadata
- Authors
- Xian, W., Huang, J.-B., Kopf, J., & Kim, C.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 9416-9426
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3184952021
- Cited by
- 357
- References
- 83
- FWCI
- 52.89
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Xian, W., Huang, J.-B., Kopf, J., & Kim, C. (2021). Space-time Neural Irradiance Fields for Free-Viewpoint Video. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00930
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