English

RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction

Computer Vision and Pattern Recognition 2026-01-22 v2 Multimedia

Abstract

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.

Keywords

Cite

@article{arxiv.2512.23437,
  title  = {RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction},
  author = {Shuhong Liu and Chenyu Bao and Ziteng Cui and Yun Liu and Xuangeng Chu and Lin Gu and Marcos V. Conde and Ryo Umagami and Tomohiro Hashimoto and Zijian Hu and Tianhan Xu and Yuan Gan and Yusuke Kurose and Tatsuya Harada},
  journal= {arXiv preprint arXiv:2512.23437},
  year   = {2026}
}