The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Point-NeRF: Point-based Neural Radiance Fields
Open accessAbstract
Volumetric neural rendering methods like NeRF [34] generate high-quality view synthesis results but are optimized per-scene leading to prohibitive reconstruction time. On the other hand, deep multi-view stereo methods can quickly reconstruct scene geometry via direct network inference. Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30× faster training time. Point-NeRF can be combined with other 3D re-construction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
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
Marching cubes: A high resolution 3D surface construction algorithm
Lorensen, W. E., & Cline, H. E. (1987) · ACM SIGGRAPH Computer Graphics · doi:10.1145/37402.37422 · Cited by 8,647
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
3D ShapeNets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., & Xiao, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298801 · Cited by 4,550
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368
Accurate, Dense, and Robust Multiview Stereopsis
Furukawa, Y., & Ponce, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.161 · Cited by 3,019
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
Cited by
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Kerbl, B., Kopanas, G., Leimkuehler, T., & Drettakis, G. (2023) · ACM Transactions on Graphics · doi:10.1145/3592433 · Cited by 6,025
“A recent approach [Xu et al. 2022] uses points to represent a radiance field with a radial basis function approach.”
MethodSplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Keetha, N., Karhade, J., Jatavallabhula, K. M., Yang, G., Scherer, S., Ramanan, D., & Luiten, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02018 · Cited by 462
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
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Yeshwanth, C., Liu, Y.-C., Nießner, M., & Dai, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00008 · Cited by 316
Compact 3D Gaussian Representation for Radiance Field
Lee, J. C., Rho, D., Sun, X., Ko, J. H., & Park, E. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02052 · Cited by 269
Metadata
- Authors
- Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., & Neumann, U.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 5428-5438
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312706422
- Cited by
- 572
- References
- 59
- FWCI
- 99.33
- Type
- conference paper
- Language
- en
- Open access
- green
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- Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., & Neumann, U. (2022). Point-NeRF: Point-based Neural Radiance Fields. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.00536
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