Summary of Spikenvs: Enhancing Novel View Synthesis From Blurry Images Via Spike Camera, by Gaole Dai and Zhenyu Wang and Qinwen Xu and Ming Lu and Wen Chen and Boxin Shi and Shanghang Zhang and Tiejun Huang
SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera
by Gaole Dai, Zhenyu Wang, Qinwen Xu, Ming Lu, Wen Chen, Boxin Shi, Shanghang Zhang, Tiejun Huang
First submitted to arxiv on: 10 Apr 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 This paper introduces a novel method for achieving sharp Novel View Synthesis (NVS) using neural field methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). The quality of training images is crucial, but conventional RGB cameras are susceptible to motion blur. Neuromorphic cameras, such as event and spike cameras, inherently capture more comprehensive temporal information, which can provide a sharp representation of the scene as additional training data. Recent methods have explored integrating event cameras to improve NVS, but these approaches have limitations like high training costs and inability to work effectively in the background. Instead, this study proposes using spike cameras to overcome these limitations by designing the Texture from Spike (TfS) loss, which considers texture reconstruction from spike streams as ground truth. The proposed method maintains manageable training costs, handles foreground objects with backgrounds simultaneously, and provides a real-world dataset captured with a spike-RGB camera system. Extensive experiments using synthetic and real-world datasets demonstrate that this design can enhance novel view synthesis across NeRF and 3DGS. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us take better pictures from new angles. It’s about making sure the pictures are sharp and clear, but right now cameras have a problem called motion blur. Some special cameras that can capture more information over time could help solve this problem. The researchers came up with a new way to use these special cameras to make the pictures even better. They tested their idea on different types of pictures and showed that it works really well. |
