Summary of Dreamsat: Towards a General 3d Model For Novel View Synthesis Of Space Objects, by Nidhi Mathihalli et al.
DreamSat: Towards a General 3D Model for Novel View Synthesis of Space Objects
by Nidhi Mathihalli, Audrey Wei, Giovanni Lavezzi, Peng Mun Siew, Victor Rodriguez-Fernandez, Hodei Urrutxua, Richard Linares
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Novel view synthesis (NVS) enables the generation of new images or conversion of 2D images into comprehensive 3D models. The application of NVS in Space Domain Awareness and Rendezvous and Proximity Operations missions can improve safety and efficiency by accurately mapping space objects and debris, and providing details about target object shape, size, and orientation for better planning and prediction. This work explores the generalization abilities of reconstruction techniques to avoid retraining for each new scene by fine-tuning a state-of-the-art single-view reconstruction model, Zero123 XL, on a high-quality dataset of spacecraft models and integrating it into the DreamGaussian framework. The approach demonstrates consistent improvements in reconstruction quality across multiple metrics, including CLIP score (+0.33%), PSNR (+2.53%), SSIM (+2.38%), and LPIPS (+0.16%) on a test set of previously unseen spacecraft images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Novel view synthesis helps create new images or turn 2D pictures into 3D models. This can be super helpful in space exploration, where it’s crucial to track objects and debris safely and efficiently. The goal is to make this process more accurate and efficient by training a model on high-quality spacecraft data. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
