Summary of Uncertainty Modeling For Fine-tuned Implicit Functions, by Anna Susmelj et al.
Uncertainty modeling for fine-tuned implicit functions
by Anna Susmelj, Mael Macuglia, Nataša Tagasovska, Reto Sutter, Sebastiano Caprara, Jean-Philippe Thiran, Ender Konukoglu
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces Dropsembles, a novel method for uncertainty estimation in tuned implicit functions such as Neural Radiance Fields (NeRFs), occupancy networks, and signed distance functions (SDFs). These models are crucial in computer vision for reconstructing detailed object shapes from sparse views. However, achieving optimal performance is challenging due to the extreme sparsity of inputs and distribution shifts induced by data corruptions. To address this issue, large synthetic datasets can serve as shape priors to help models fill in gaps. The paper highlights the importance of uncertainty estimation for assessing the quality of these reconstructions, particularly in identifying areas where the model is uncertain about the parts it has inferred from the prior. The proposed method is demonstrated through experiments on toy examples and a real-world scenario, achieving the accuracy and calibration levels of deep ensembles with significantly less computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer vision models more accurate. These models are used to recreate shapes from pictures taken from different angles. The problem is that these models can be uncertain about parts they’ve inferred, so we need a way to measure this uncertainty. The authors introduce “Dropsembles” which helps with this uncertainty estimation in models like Neural Radiance Fields (NeRFs). They show it works well on small examples and even on real-world MRI images of the spine. |
