While feed-forward 3D reconstruction models have advanced rapidly, they still exhibit degraded performance on panoramas due to spherical distortions. Moreover, existing panoramic 3D datasets are predominantly collected with 360 cameras fixed at discrete locations, resulting in discontinuous trajectories. These limitations critically hinder the development of panoramic feed-forward 3D reconstruction, especially for the multi-view setting. In this paper, we present Holo360D, a comprehensive dataset containing 109,495 panoramas paired with registered point clouds, meshes, and aligned camera poses. To our knowledge, Holo360D is the first large-scale dataset that provides continuous panoramic sequences with accurately aligned high-completeness depth maps. The raw data are initially collected using a 3D laser scanner coupled with a 360 camera. Subsequently, the raw data are processed with both online and offline SLAM systems. Furthermore, to enhance the 3D data quality, a post-processing pipeline tailored for the 360 dataset is proposed, including geometry denoising, mesh hole filling, and region-specific remeshing. Finally, we establish a new benchmark by fine-tuning 3D reconstruction models on Holo360D, providing key insights into effective fine-tuning strategies. Our results demonstrate that Holo360D delivers superior training signals and provides a comprehensive benchmark for advancing panoramic 3D reconstruction models. Datasets and Code will be made publicly available.

翻译：虽然前馈式三维重建模型取得了快速进展，但由于球面畸变的影响，它们在处理全景图像时性能仍然不佳。此外，现有的全景三维数据集主要使用固定于离散位置的360°相机采集，导致轨迹不连续。这些局限性严重阻碍了全景前馈式三维重建的发展，尤其是在多视角设定下。本文提出Holo360D，一个包含109,495张全景图像及其配准的点云、网格和对齐相机位姿的综合数据集。据我们所知，Holo360D是首个提供连续全景序列并配有精确对齐的高完整性深度图的大规模数据集。原始数据通过三维激光扫描仪与360°相机联合采集，随后通过在线与离线SLAM系统进行处理。为了进一步提升三维数据质量，我们提出了一套针对360°数据集的后处理流程，包括几何去噪、网格孔洞填充及区域特定重网格化。最后，通过在Holo360D上微调三维重建模型，我们建立了一个新的基准，并揭示了有效的微调策略的关键见解。实验结果表明，Holo360D能提供更优的训练信号，并为推动全景三维重建模型的发展提供了一个全面的基准。数据集与代码将公开发布。