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].

在空间智能（Spatial Intelligence）飞速发展的今天，全景视角因其 360° 的环绕覆盖能力，成为了机器人导航、自动驾驶及虚拟现实的核心基石。然而，全景深度 估计长期面临 "数据荒" 与 "模型泛化差" 的瓶颈。 近日， 来自 Insta360 研究团队、加州大学圣地亚哥分校 (UCSD)、武汉大学以及加州大学默塞德分校的研究者 共同推出了 Depth Any Panora mas (DAP) 。这是首 个在大规模多样化数据集上训练的全景度量深度（Metric Depth）基础模型，不仅统一了室内外场景，更通过 200 万量级的数据引擎与创新的几何一致性设计，刷 新了多项 benchmark 纪录，在多种 open-world 场景下保持优异的效果。 模型对由 Gemini 或 DiT-360 等合成的全景图同样展现出了极佳的预测效果，生成的深度图边缘锐利、逻辑自洽，是空间 AIGC 链路中理想的几何基石。 除了静态 图像，DAP 在处理全景视频流时同样展现出了极佳的预测效果，具备优秀的帧间一致性与稳定性 。 论文标题：Depth Any Panoramas: A Foundation Mod ...