Summary of Dense Depth From Event Focal Stack, by Kenta Horikawa and Mariko Isogawa and Hideo Saito and Shohei Mori
Dense Depth from Event Focal Stack
by Kenta Horikawa, Mariko Isogawa, Hideo Saito, Shohei Mori
First submitted to arxiv on: 11 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 for dense depth estimation from an event stream uses a convolutional neural network trained with synthesized event focal stacks to infer a depth map. This approach is trained on scenes with diverse structures using Blender-generated 3D scenes, allowing for more comprehensive training data. The method also explores methods to eliminate the domain gap between real and synthetic event streams, resulting in superior performance compared to a depth-from-defocus method in both synthetic and real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This innovative approach helps us better understand the world around us by accurately estimating depths from event streams. By using convolutional neural networks and synthesized data, we can create more comprehensive training sets that reflect diverse real-world scenarios. This technology has exciting potential applications, such as advanced computer vision and robotics. |
Keywords
» Artificial intelligence » Depth estimation » Neural network
