Summary of Synergistic Integration Of Coordinate Network and Tensorial Feature For Improving Neural Radiance Fields From Sparse Inputs, by Mingyu Kim et al.
Synergistic Integration of Coordinate Network and Tensorial Feature for Improving Neural Radiance Fields from Sparse Inputs
by Mingyu Kim, Jun-Seong Kim, Se-Young Yun, Jin-Hwa Kim
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed method synergistically integrates multi-plane representation with a coordinate-based MLP network to capture low-frequency details while preserving fine-grained details. The multi-plane representation is biased toward fine details, which can lead to instability and inefficiency when training poses are sparse. To address this issue, the authors propose a progressive training scheme that accelerates the disentanglement of these two features. The method outperforms baseline models for both static and dynamic NeRFs with sparse inputs and achieves comparable results with fewer parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about improving a way to create images from 3D data called neural radiance fields (NeRF). Right now, this method works well when there are many points in the scene that can be used as reference. But what if there are very few points? The current approach doesn’t do well in those situations. The authors of this paper propose a new way to combine two existing methods to create images from 3D data that performs better than the current method, especially when there are few points to use. |
