The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Block-NeRF: Scalable Large Scene Neural View Synthesis
Open accessAbstract
We present Block-NeRF, a variant of Neural Radiance Fields that can represent large-scale environments. Specifically, we demonstrate that when scaling NeRF to render city-scale scenes spanning multiple blocks, it is vital to de-compose the scene into individually trained NeRFs. This decomposition decouples rendering time from scene size, enables rendering to scale to arbitrarily large environments, and allows per-block updates of the environment. We adopt several architectural changes to make NeRF robust to data captured over months under different environmental conditions. We add appearance embeddings, learned pose refinement, and controllable exposure to each individual NeRF, and introduce a procedure for aligning appearance between adjacent NeRFs so that they can be seamlessly combined. We build a grid of Block-NeRFs from 2.8 million images to create the largest neural scene representation to date, capable of rendering an entire neighborhood of San Francisco.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
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The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
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
Self-citationInstant neural graphics primitives with a multiresolution hash encoding
Müller, T., Evans, A., Schied, C., & Keller, A. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530127 · Cited by 4,114
Bundle Adjustment — A Modern Synthesis
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Cited by
Data augmentation: A comprehensive survey of modern approaches
Mumuni, A., & Mumuni, F. (2022) · Array · doi:10.1016/j.array.2022.100258 · Cited by 891
Objaverse: A Universe of Annotated 3D Objects
Deitke, M., Schwenk, D., Salvador, J., Weihs, L., Michel, O., VanderBilt, E., Schmidt, L., Ehsanit, K., Kembhavi, A., & Farhadi, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01263 · Cited by 725
“Photogrammetry and NERF advances have enabled the commercialization of capturing high-quality 3D objects of large exteriors by taking pictures [75, 85].”
BackgroundK-Planes: Explicit Radiance Fields in Space, Time, and Appearance
Fridovich-Keil, S., Meanti, G., Warburg, F. R., Recht, B., & Kanazawa, A. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01201 · Cited by 560
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
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- Throughs
Turki, H., Ramanan, D., & Satyanarayanan, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01258 · Cited by 423
Metadata
- Authors
- Tancik, M., Casser, V., Yan, X., Pradhan, S., Mildenhall, B. P., Srinivasan, P., Barron, J. T., & Kretzschmar, H.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 8238-8248
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312280420
- Cited by
- 781
- References
- 68
- FWCI
- 134.23
- Type
- conference paper
- Language
- en
- Open access
- green
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