Summary of Sat-ngp : Unleashing Neural Graphics Primitives For Fast Relightable Transient-free 3d Reconstruction From Satellite Imagery, by Camille Billouard et al.
SAT-NGP : Unleashing Neural Graphics Primitives for Fast Relightable Transient-Free 3D reconstruction from Satellite Imagery
by Camille Billouard, Dawa Derksen, Emmanuelle Sarrazin, Bruno Vallet
First submitted to arxiv on: 27 Mar 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 Medium Difficulty summary: This paper proposes a novel approach for efficient 3D reconstruction from multi-date satellite images using Neural Radiance Fields (NeRF). The current stereo-vision pipelines achieve high accuracy but are sensitive to changes between images caused by variable shadows, reflections, and transient objects. To overcome this limitation, the authors adopt Instant Neural Graphics Primitives and introduce Satellite Neural Graphics Primitives (SAT-NGP), which employs efficient sampling strategies and multi-resolution hash encoding to accelerate learning. SAT-NGP reduces the learning time from dozens of hours to 15 minutes while maintaining the quality of 3D reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research paper talks about a new way to create 3D models from satellite images taken at different times. Right now, this process is slow and sensitive to changes in the images, like shadows or moving objects. The authors want to make it faster and more reliable by using a special kind of AI called Neural Radiance Fields (NeRF). They came up with an idea to speed up the process using something called Satellite Neural Graphics Primitives (SAT-NGP), which makes learning much quicker without sacrificing quality. |
