RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction
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arXiv
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| Format: | Preprint |
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2025
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| author | Liu, Shuhong Bao, Chenyu Cui, Ziteng Liu, Yun Chu, Xuangeng Gu, Lin Conde, Marcos V. Umagami, Ryo Hashimoto, Tomohiro Hu, Zijian Xu, Tianhan Gan, Yuan Kurose, Yusuke Harada, Tatsuya |
| author_facet | Liu, Shuhong Bao, Chenyu Cui, Ziteng Liu, Yun Chu, Xuangeng Gu, Lin Conde, Marcos V. Umagami, Ryo Hashimoto, Tomohiro Hu, Zijian Xu, Tianhan Gan, Yuan Kurose, Yusuke Harada, Tatsuya |
| contents | We introduce RealX3D, a real-capture benchmark for multi-view visual restoration and 3D reconstruction under diverse physical degradations. RealX3D groups corruptions into four families, including illumination, scattering, occlusion, and blurring, and captures each at multiple severity levels using a unified acquisition protocol that yields pixel-aligned LQ/GT views. Each scene includes high-resolution capture, RAW images, and dense laser scans, from which we derive world-scale meshes and metric depth. Benchmarking a broad range of optimization-based and feed-forward methods shows substantial degradation in reconstruction quality under physical corruptions, underscoring the fragility of current multi-view pipelines in real-world challenging environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction Liu, Shuhong Bao, Chenyu Cui, Ziteng Liu, Yun Chu, Xuangeng Gu, Lin Conde, Marcos V. Umagami, Ryo Hashimoto, Tomohiro Hu, Zijian Xu, Tianhan Gan, Yuan Kurose, Yusuke Harada, Tatsuya Computer Vision and Pattern Recognition Multimedia We introduce RealX3D, a real-capture benchmark for multi-view visual restoration and 3D reconstruction under diverse physical degradations. RealX3D groups corruptions into four families, including illumination, scattering, occlusion, and blurring, and captures each at multiple severity levels using a unified acquisition protocol that yields pixel-aligned LQ/GT views. Each scene includes high-resolution capture, RAW images, and dense laser scans, from which we derive world-scale meshes and metric depth. Benchmarking a broad range of optimization-based and feed-forward methods shows substantial degradation in reconstruction quality under physical corruptions, underscoring the fragility of current multi-view pipelines in real-world challenging environments. |
| title | RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2512.23437 |