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

Abstract Reliable 3D reconstruction is a prerequisite for robotics, embodied AI, and immersive AR/VR applications; however, real-world observations frequently depart from clean imaging assumptions due to illumination changes, participating media, occlusions, and blur that break multi-view consistency and destabilize pose estimation, which leaves a gap between performance on curated benchmarks and behavior in practical deployments. To address this gap, we introduce RealX3D, a real capture benchmark for multi-view restoration and reconstruction under real-world degradations, organized into four families spanning nine controlled settings that include motion and defocus blur, low-light, view-varying exposure, smoke, dynamic occlusion, and reflection. RealX3D is collected using a unified acquisition protocol that enables recapturing the same camera trajectories to obtain pixel-aligned low-quality and reference ground-truth image pairs. Each scene also provides per-view RAW measurements to preserve high dynamic range linear sensor signals. To support geometry-grounded evaluation beyond image photometric fidelity, we capture dense laser scan geometry for every scene and derive world-scale measures such as point clouds, meshes, and metric depth, allowing comprehensive assessment of pose, depth, and surface reconstruction alongside photometric restoration quality. The benchmark contains 55 scenes recorded at high resolution with diverse real-world degradation patterns. We benchmark a broad set of optimization-based and feed-forward methods using both image metrics and geometry metrics, and the results reveal substantial robustness gaps across degradations in adverse conditions. Overall, RealX3D provides a rigorous benchmark that moves beyond synthetic data and establishes a standardized foundation for developing degradation-robust 3D reconstruction systems.