The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
iNeRF: Inverting Neural Radiance Fields for Pose Estimation
Open accessAbstract
We present iNeRF, a framework that performs mesh-free pose estimation by "inverting" a Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the task of view synthesis — synthesizing photorealistic novel views of real-world scenes or objects. In this work, we investigate whether we can apply analysis-by-synthesis via NeRF for mesh-free, RGB-only 6DoF pose estimation – given an image, find the translation and rotation of a camera relative to a 3D object or scene. Our method assumes that no object mesh models are available during either training or test time. Starting from an initial pose estimate, we use gradient descent to minimize the residual between pixels rendered from a NeRF and pixels in an observed image. In our experiments, we first study 1) how to sample rays during pose refinement for iNeRF to collect informative gradients and 2) how different batch sizes of rays affect iNeRF on a synthetic dataset. We then show that for complex real-world scenes from the LLFF dataset [21], iNeRF can improve NeRF by estimating the camera poses of novel images and using these images as additional training data for NeRF. Finally, we show iNeRF can perform categorylevel object pose estimation, including object instances not seen during training, with RGB images by inverting a NeRF model inferred from a single view.
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Cites
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
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Self-citationDeepSDF: 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
Self-citationSuperPoint: Self-Supervised Interest Point Detection and Description
DeTone, D., Malisiewicz, T., & Rabinovich, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00060 · Cited by 3,355
SuperGlue: Learning Feature Matching With Graph Neural Networks
Sarlin, P.-E., DeTone, D., Malisiewicz, T., & Rabinovich, A. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00499 · Cited by 2,830
Occupancy Networks: Learning 3D Reconstruction in Function Space
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
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Image Alignment and Stitching: A Tutorial
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pixelNeRF: Neural Radiance Fields from One or Few Images
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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
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
Rosinol, A., Leonard, J. J., & Carlone, L. (2023) · 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341922 · Cited by 281
Metadata
- Authors
- Yen-Chen, L., Florence, P., Barron, J. T., Rodriguez, A., Isola, P., & Lin, T.-Y.
- Venue
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2021-09-27
- Pages
- 1323-1330
- OpenAlex
- W3112108866
- Cited by
- 404
- References
- 52
- FWCI
- 49.27
- Type
- conference paper
- Language
- en
- Open access
- green
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