Summary of Sources Of Uncertainty in 3d Scene Reconstruction, by Marcus Klasson et al.
Sources of Uncertainty in 3D Scene Reconstruction
by Marcus Klasson, Riccardo Mereu, Juho Kannala, Arno Solin
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a taxonomy to categorize uncertainty sources in Neural Radiance Fields (NeRFs) and 3D Gaussian Splatting (GS), which are used for high-fidelity rendering. The authors identify noise, occlusions, confounding outliers, and imprecise camera pose inputs as common uncertainty sources affecting the reconstruction process. To address these uncertainties, they extend NeRF- and GS-based methods with uncertainty estimation techniques, such as learning uncertainty outputs and ensembles. An empirical study is conducted to evaluate the effectiveness of these methods in capturing the sensitivity of the reconstruction. The paper highlights the importance of considering various uncertainty aspects when designing NeRF/GS-based methods for uncertainty-aware 3D reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how to create more accurate and reliable 3D models from real-world scenes. Currently, there are some methods that can make high-quality 3D models, but they don’t account for uncertainties like noise, missing information, or camera position errors. The authors of this paper develop a way to categorize these uncertainty sources and introduce techniques to estimate the uncertainty in the model. They test their approach and show how it can improve the accuracy of the reconstructed 3D models. |
