Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation
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: https://insta360-research-team.github.io/DAP_website/ {https://insta360-research-team.github.io/DAP\_website/}
6944c315fbf17e708e18604a
Xin Lin
6944c315fbf17e708e18604b
Meixi Song
6944c315fbf17e708e18604c
Dizhe Zhang
6944c315fbf17e708e18604d
Wenxuan Lu
6944c315fbf17e708e18604e
Haodong Li
claimed_verified
2025-12-19T08:57:41.262Z
641d211e353524fe41f16387
/avatars/c6e72c82c029b415a035beebee50b52c.svg
Haodong Li
haodongli
user
haodongli
6944c315fbf17e708e18604f
Bo Du
6944c315fbf17e708e186050
Ming-Hsuan Yang
6944c315fbf17e708e186051
Truong Nguyen
6944c315fbf17e708e186052
Lu Qi
Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation
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: https://insta360-research-team.github.io/DAP_website/ {https://insta360-research-team.github.io/DAP\_website/}
A panoramic metric depth foundation model using DINOv3-Large and a three-stage pseudo-label pipeline achieves robust performance across diverse real-world scenes.
DINOv3-Large pseudo-label curation pipeline range mask head sharpness-centric optimization geometry-centric optimization Stanford2D3D Matterport3D Deep360