RealX3D: A Physically-Degraded 3D Benchmark for Multi-view Visual Restoration and Reconstruction
This addresses the fragility of multi-view pipelines in real-world challenging environments for computer vision researchers, though it is incremental as it provides a new benchmark rather than a novel method.
The authors tackled the problem of multi-view visual restoration and 3D reconstruction under physical degradations by introducing RealX3D, a real-capture benchmark with diverse corruptions, and found that current methods suffer substantial degradation in reconstruction quality in such environments.
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.