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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.01079 |
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| _version_ | 1866918111865733120 |
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| author | Diaz, Santiago Hu, Xinghui Uwumukiza, Josiane Lavezzi, Giovanni Rodriguez-Fernandez, Victor Linares, Richard |
| author_facet | Diaz, Santiago Hu, Xinghui Uwumukiza, Josiane Lavezzi, Giovanni Rodriguez-Fernandez, Victor Linares, Richard |
| contents | To enhance asteroid exploration and autonomous spacecraft navigation, we introduce DreamSat-2.0, a pipeline that benchmarks three state-of-the-art 3D reconstruction models-Hunyuan-3D, Trellis-3D, and Ouroboros-3D-on custom spacecraft and asteroid datasets. Our systematic analysis, using 2D perceptual (image quality) and 3D geometric (shape accuracy) metrics, reveals that model performance is domain-dependent. While models produce higher-quality images of complex spacecraft, they achieve better geometric reconstructions for the simpler forms of asteroids. New benchmarks are established, with Hunyuan-3D achieving top perceptual scores on spacecraft but its best geometric accuracy on asteroids, marking a significant advance over our prior work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01079 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DreamSat-2.0: Towards a General Single-View Asteroid 3D Reconstruction Diaz, Santiago Hu, Xinghui Uwumukiza, Josiane Lavezzi, Giovanni Rodriguez-Fernandez, Victor Linares, Richard Computer Vision and Pattern Recognition Machine Learning To enhance asteroid exploration and autonomous spacecraft navigation, we introduce DreamSat-2.0, a pipeline that benchmarks three state-of-the-art 3D reconstruction models-Hunyuan-3D, Trellis-3D, and Ouroboros-3D-on custom spacecraft and asteroid datasets. Our systematic analysis, using 2D perceptual (image quality) and 3D geometric (shape accuracy) metrics, reveals that model performance is domain-dependent. While models produce higher-quality images of complex spacecraft, they achieve better geometric reconstructions for the simpler forms of asteroids. New benchmarks are established, with Hunyuan-3D achieving top perceptual scores on spacecraft but its best geometric accuracy on asteroids, marking a significant advance over our prior work. |
| title | DreamSat-2.0: Towards a General Single-View Asteroid 3D Reconstruction |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.01079 |