{"ID":2827723,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16913","arxiv_id":"2512.16913","title":"Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation","abstract":"In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5 simulator and text-to-image models, and real panoramic images from the web. To reduce domain gaps between indoor/outdoor and synthetic/real data, we introduce a three-stage pseudo-label curation pipeline to generate reliable ground truth for unlabeled images. For the model, we adopt DINOv3-Large as the backbone for its strong pre-trained generalization, and introduce a plug-and-play range mask head, sharpness-centric optimization, and geometry-centric optimization to improve robustness to varying distances and enforce geometric consistency across views. Experiments on multiple benchmarks (e.g., Stanford2D3D, Matterport3D, and Deep360) demonstrate strong performance and zero-shot generalization, with particularly robust and stable metric predictions in diverse real-world scenes. The project page can be found at: \\href{https://insta360-research-team.github.io/DAP_website/} {https://insta360-research-team.github.io/DAP\\_website/}","short_abstract":"In this work, we present a panoramic metric depth foundation model that generalizes across diverse scene distances. We explore a data-in-the-loop paradigm from the view of both data construction and framework design. We collect a large-scale dataset by combining public datasets, high-quality synthetic data from our UE5...","url_abs":"https://arxiv.org/abs/2512.16913","url_pdf":"https://arxiv.org/pdf/2512.16913v1","authors":"[\"Xin Lin\",\"Meixi Song\",\"Dizhe Zhang\",\"Wenxuan Lu\",\"Haodong Li\",\"Bo Du\",\"Ming-Hsuan Yang\",\"Truong Nguyen\",\"Lu Qi\"]","published":"2025-12-18T18:59:29Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}