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

Shuhong Liu; Chenyu Bao; Ziteng Cui; Yun Liu; Xuangeng Chu; Lin Gu; Marcos V. Conde; Ryo Umagami; Tomohiro Hashimoto; Zijian Hu; Tianhan Xu; Yuan Gan; Yusuke Kurose; Tatsuya Harada

arXiv · 2512.23437

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

Shuhong Liu·Chenyu Bao·Ziteng Cui·Yun Liu·Xuangeng Chu·Lin Gu·Marcos V. Conde·Ryo Umagami·Tomohiro Hashimoto·Zijian Hu·Tianhan Xu·Yuan Gan·Yusuke Kurose·Tatsuya Harada

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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.

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Shuhong Liu, Chenyu Bao, Ziteng Cui, Yun Liu, Xuangeng Chu, Lin Gu, Marcos V. Conde, Ryo Umagami, Tomohiro Hashimoto, Zijian Hu, Tianhan Xu, Yuan Gan, Yusuke Kurose, Tatsuya Harada. 2025-12-29. RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction. https://arxiv.org/abs/2512.23437

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