Core Insights - The article discusses the launch of Depth Any Panoramas (DAP), a foundational model for panoramic depth estimation, which addresses the challenges of data scarcity and model generalization in spatial intelligence [1][19]. Data and Model Development - DAP is trained on an unprecedented scale of 2 million (2M) panoramic images, significantly surpassing previous datasets like Stanford2D3D and Matterport3D, which had only tens of thousands of images [6][7]. - The model utilizes a three-stage pseudo-labeling pipeline to refine the quality of depth estimation from unlabelled panoramic images, ultimately creating a robust training dataset [10][11]. Performance and Benchmarking - DAP has demonstrated superior performance in various benchmarks, achieving significant reductions in absolute relative error (AbsRel) and root mean square error (RMSE) across indoor and outdoor datasets [14][17]. - In zero-shot testing, DAP outperformed existing models, showcasing its strong generalization capabilities and effective depth prediction in complex environments [13][16]. Technological Innovations - The model incorporates advanced features such as a distance-adaptive range mask head, allowing it to adjust depth perception based on different application scenarios [16]. - DAP employs multi-dimensional geometric optimization techniques to ensure sharp edges and accurate geometric structures in depth maps, addressing common issues like depth holes and structural distortion [16]. Industry Implications - The introduction of DAP marks a significant milestone in panoramic depth estimation, enabling advancements in autonomous driving, robotics, and VR/AR content creation by providing a low-cost method for depth acquisition [19][20]. - The project has been made open-source, allowing broader access to its technology and fostering further innovation in the field of spatial intelligence [20].