# DA2: Depth Anything in Any Direction

arXiv: https://arxiv.org/abs/2509.26618 · HTML: https://mlsanity.com/paper/2509.26618/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 73 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 30 of 62)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://mlsanity.com/paper/2307.10984/) (2023)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://mlsanity.com/paper/2312.02145/) (2023)
- [MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision](https://mlsanity.com/paper/2410.19115/) (2024)
- [LucidDreamer: Towards High-Fidelity Text-to-3D Generation via Interval Score Matching](https://mlsanity.com/paper/2311.11284/) (2023)
- [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://mlsanity.com/paper/1908.00222/) (2019)
- [DeepMVS: Learning Multi-view Stereopsis](https://mlsanity.com/paper/1804.00650/) (2018)
- [MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details](https://mlsanity.com/paper/2507.02546/) (2025)
- [UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler](https://mlsanity.com/paper/2502.20110/) (2025)
- [Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction](https://mlsanity.com/paper/2409.18124/) (2024)
- [DynamicStereo: Consistent Dynamic Depth from Stereo Videos](https://mlsanity.com/paper/2305.02296/) (2023)
- [3D Ken Burns Effect from a Single Image](https://mlsanity.com/paper/1909.05483/) (2019)

## Built on by (most cited first, 25 of 25)

- [Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation](https://mlsanity.com/paper/2512.16913/) (2025)
- [Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model](https://mlsanity.com/paper/2512.01030/) (2025)
- [PanoWorld: Towards Spatial Supersensing in 360^circ Panorama World](https://mlsanity.com/paper/2605.13169/) (2026)
- [More than the Sum: Panorama-Language Models for Adverse Omni-Scenes](https://mlsanity.com/paper/2603.09573/) (2026)
- [OpenWorldLib: A Unified Codebase and Definition of Advanced World Models](https://mlsanity.com/paper/2604.04707/) (2026)
- [Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models](https://mlsanity.com/paper/2604.10578/) (2026)
- [φ-Scene: Physically Grounded Image-to-3D Scene Reconstruction](https://mlsanity.com/paper/2606.21596/) (2026)
- [EAGOR: Embodied Reasoning in Omni-direction](https://mlsanity.com/paper/2607.06165/) (2026)
- [X-Lens: Real-Time Metric Depth Estimation with Heterogeneous Cameras](https://mlsanity.com/paper/2607.12993/) (2026)
- [Depth Anything in 360^circ: Towards Scale Invariance in the Wild](https://mlsanity.com/paper/2512.22819/) (2025)
- [Unified Panoramic Geometry Estimation via Multi-View Foundation Models](https://mlsanity.com/paper/2605.26368/) (2026)
- [Argus: Metric Panoramic 3D Reconstruction for Indoor Scenes](https://mlsanity.com/paper/2606.30047/) (2026)
- [CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction](https://mlsanity.com/paper/2606.31086/) (2026)
- [OmniPoint: Universal Monocular Metric Pointcloud from Any Camera](https://mlsanity.com/paper/2609.09394/) (2026)
- [PAGCNet: A Pose-Aware and Geometry Constrained Framework for Panoramic Depth Estimation](https://mlsanity.com/paper/2510.07817/) (2025)
- [FlatLands: Generative Floormap Completion From a Single Egocentric View](https://mlsanity.com/paper/2603.16016/) (2026)
- [Pose-Free Omnidirectional Gaussian Splatting for 360-Degree Videos with Consistent Depth Priors](https://mlsanity.com/paper/2603.23324/) (2026)
- [H-OmniStereo: Zero-Shot Omnidirectional Stereo Matching with Heading-Aligned Normal Priors](https://mlsanity.com/paper/2605.14963/) (2026)
- [DepthMaster: Unified Monocular Depth Estimation for Perspective and Panoramic Images](https://mlsanity.com/paper/2606.12368/) (2026)
- [CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking](https://mlsanity.com/paper/2606.30097/) (2026)
- [Monocular Depth Estimation from a Single Image: Progress and Opportunities](https://mlsanity.com/paper/2609.01172/) (2026)
- [PanoVLN: Towards Effective Panoramic Vision-and-Language Navigation](https://mlsanity.com/paper/2609.34759/) (2026)
- [Many Eyes, One World: Feed-Forward 3D Reconstruction from Mixed Cameras](https://mlsanity.com/paper/2609.35658/) (2026)
- [How to Reduce Localization Ambiguity? Geometry-Semantic Constrained BEV Representation Learning for Satellite-Ground Localization](https://mlsanity.com/paper/2609.39127/) (2026)
- [OmniAct3D: Leveraging Foundation Geometry and Evidence-Grounded Reasoning for Panoramic 3D Detection](https://mlsanity.com/paper/2610.03015/) (2026)

## Abstract (arXiv)

Panorama has a full FoV (360^circ×180^circ), offering a more complete visual description than perspective images. Thanks to this characteristic, panoramic depth estimation is gaining increasing traction in 3D vision. However, due to the scarcity of panoramic data, previous methods are often restricted to in-domain settings, leading to poor zero-shot generalization. Furthermore, due to the spherical distortions inherent in panoramas, many approaches rely on perspective splitting (e.g., cubemaps), which leads to suboptimal efficiency. To address these challenges, we propose DA2: Depth Anything in Any Direction, an accurate, zero-shot generalizable, and fully end-to-end panoramic depth estimator. Specifically, for scaling up panoramic data, we introduce a data curation engine for generating high-quality panoramic depth data from perspective, and create ~543K panoramic RGB-depth pairs, bringing the total to ~607K. To further mitigate the spherical distortions, we present SphereViT, which explicitly leverages spherical coordinates to enforce the spherical geometric consistency in panoramic image features, yielding improved performance. A comprehensive benchmark on multiple datasets clearly demonstrates DA2's SoTA performance, with an average 38% improvement on AbsRel over the strongest zero-shot baseline. Surprisingly, DA2 even outperforms prior in-domain methods, highlighting its superior zero-shot generalization. Moreover, as an end-to-end solution, DA2 exhibits much higher efficiency over fusion-based approaches. Both the code and the curated panoramic data has be released. Project page: https://depth-any-in-any-dir.github.io/.