OmniLife360: A Benchmark for 3D Reconstruction from In-the-Wild 360° Captures

The widespread adoption of consumer 360° cameras has created a growing demand for robust in-the-wild panoramic 3D reconstruction. Existing methods remain fragile on long-horizon trajectories with strong ego-motion and dynamic interference. Quantifying this gap at scale is challenging because available benchmarks are often short, constrained, or captured in controlled settings, limiting evaluation and progress. To address this, we introduce OmniLife360, a benchmark for 3D reconstruction from in-the-wild 360° captures. It spans diverse scenes, environments, and motion dynamics captured by individuals across real-world activities, enabling rigorous evaluation of reconstruction robustness in complex natural conditions. Extensive evaluations reveal that state-ofthe-art methods excel in controlled settings but suffer significant degradation in dynamic scenarios. Motivated by these findings, we propose Omni4DGS, a motion-aware Gaussian Splatting framework that combines motion decomposition with lightweight supervision to better handle motion variations, dynamic distractors, and long-horizon pose drift. Experiments show that Omni4DGS consistently improves reconstruction quality and stability in dynamic 360° scenarios, reducing motion-induced artifacts and producing cleaner renderings under strong scene motion. The benchmark is available at https://huggingface.co/datasets/ AutoLab-SJTU/OmniLife360.