The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Local Implicit Grid Representations for 3D Scenes
Open accessAbstract
Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D autoencoders cannot handle their scale, complexity, or diversity. In this paper, we introduce Local Implicit Grid Representations, a new 3D shape representation designed for scalability and generality. The motivating idea is that most 3D surfaces share geometric details at some scale - i.e., at a scale smaller than an entire object and larger than a small patch. We train an autoencoder to learn an embedding of local crops of 3D shapes at that size. Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation. We demonstrate the value of this proposed approach for 3D surface reconstruction from sparse point observations, showing significantly better results than alternative approaches.
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Cites
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Dynamic 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
MeshCNN: a network with an edge
Hanocka, R., Hertz, A., Fish, N., Giryes, R., Fleishman, S., & Cohen-Or, D. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322959 · Cited by 742
Geodesic Convolutional Neural Networks on Riemannian Manifolds
Masci, J., Boscaini, D., Bronstein, M. M., & Vandergheynst, P. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.112 · Cited by 721
PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows
Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., & Hariharan, B. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00464 · Cited by 656
Cited by
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
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Tancik, M., Srinivasan, P. P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J. T., & Ng, R. (2020) · arXiv · doi:10.48550/arxiv.2006.10739 · Cited by 1,459
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
Chan, E. R., Monteiro, M., Kellnhofer, P., Wu, J., & Wetzstein, G. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00574 · Cited by 689
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Chabra, R., Lenssen, J. E., Ilg, E., Schmidt, T., Straub, J., Lovegrove, S., & Newcombe, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58526-6_36 · Cited by 380
Neural Articulated Radiance Field
Noguchi, A., Sun, X., Lin, S., & Harada, T. (2021) · arXiv · doi:10.48550/arxiv.2104.03110 · Cited by 177
Metadata
- Authors
- Jiang, C., Sud, A., Makadia, A., Huang, J., NieBner, M., & Funkhouser, T.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6000-6009
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3012728190
- Cited by
- 526
- References
- 5
- FWCI
- 75.97
- Type
- conference paper
- Language
- en
- Open access
- green
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- Jiang, C., Sud, A., Makadia, A., Huang, J., NieBner, M., & Funkhouser, T. (2020). Local Implicit Grid Representations for 3D Scenes. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00604
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