Holo360D: A Large-Scale Real-World Dataset with Continuous Trajectories for Advancing Panoramic 3D Reconstruction and Beyond

While feed-forward 3D reconstruction models have advancedrapidly, they still exhibit degraded performance on panoramas due tospherical distortions. Moreover, existing panoramic 3D datasets are pre-dominantly collected with 360◦ cameras fixed at discrete locations, re-sulting in discontinuous trajectories. These limitations critically hin-der the development of panoramic feed-forward 3D reconstruction, espe-cially for the multi-view setting. In this paper, we present Holo360D,a comprehensive dataset containing 109,495 panoramas paired with reg-istered point clouds, meshes, and aligned camera poses. To our knowl-edge, Holo360D is the first large-scale dataset that provides continuouspanoramic sequences with accurately aligned high-completenessdepth maps. The raw data are initially collected using a 3D laser scan-ner coupled with a 360◦ camera. Subsequently, the raw data are pro-cessed with both online and offline SLAM systems. Furthermore, to en-hance the 3D data quality, a post-processing pipeline tailored for the 360◦dataset is proposed, including geometry denoising, mesh hole filling, andregion-specific remeshing, etc. Finally, we establish a new benchmark byfine-tuning 3D reconstruction models on Holo360D, providing key in-sights into effective fine-tuning strategies. Our results demonstrate thatHolo360D delivers superior training signals and provides a comprehensivebenchmark for advancing panoramic 3D reconstruction models. Datasetsand Code will be made publicly available. Dataset Page: Holo360D