# High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registration

arXiv: https://arxiv.org/abs/2210.10414 · HTML: https://mlsanity.com/paper/2210.10414/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 48 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 30 of 31)

- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Semantic Scene Completion from a Single Depth Image](https://mlsanity.com/paper/1611.08974/) (2016)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling](https://mlsanity.com/paper/1908.00222/) (2019)
- [Learning to Recover 3D Scene Shape from a Single Image](https://mlsanity.com/paper/2012.09365/) (2020)
- [HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation](https://mlsanity.com/paper/1901.03861/) (2019)
- [HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features](https://mlsanity.com/paper/2011.11498/) (2020)
- [Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging](https://mlsanity.com/paper/2105.14021/) (2021)
- [OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas](https://mlsanity.com/paper/1807.09620/) (2018)
- [LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image](https://mlsanity.com/paper/1803.08999/) (2018)
- [PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation](https://mlsanity.com/paper/2203.09283/) (2022)
- [UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimation](https://mlsanity.com/paper/2102.03550/) (2021)
- [Guiding Monocular Depth Estimation Using Depth-Attention Volume](https://mlsanity.com/paper/2004.02760/) (2020)
- [Tangent Images for Mitigating Spherical Distortion](https://mlsanity.com/paper/1912.09390/) (2019)
- [DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama](https://mlsanity.com/paper/1811.11977/) (2018)
- [OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion](https://mlsanity.com/paper/2203.00838/) (2022)
- [Spherical View Synthesis for Self-Supervised 360 Depth Estimation](https://mlsanity.com/paper/1909.08112/) (2019)
- [One Shot 3D Photography](https://mlsanity.com/paper/2008.12298/) (2020)
- [LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering](https://mlsanity.com/paper/2104.00568/) (2021)
- [OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems](https://mlsanity.com/paper/2003.08056/) (2020)
- [Layout-Guided Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2103.17022/) (2021)
- [Mapped Convolutions](https://mlsanity.com/paper/1906.11096/) (2019)
- [PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation](https://mlsanity.com/paper/2203.15965/) (2022)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)

## Built on by (most cited first, 11 of 11)

- [Depth Any Panoramas: A Foundation Model for Panoramic Depth Estimation](https://mlsanity.com/paper/2512.16913/) (2025)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [Taming Stable Diffusion for Text to 360deg Panorama Image Generation](https://mlsanity.com/paper/2404.07949/) (2024)
- [SGFormer: Spherical Geometry Transformer for 360 Depth Estimation](https://mlsanity.com/paper/2404.14979/) (2024)
- [PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction](https://mlsanity.com/paper/2507.21960/) (2025)
- [VGGT-360: Geometry-Consistent Zero-Shot Panoramic Depth Estimation](https://mlsanity.com/paper/2603.18943/) (2026)
- [RPG360: Robust 360 Depth Estimation with Perspective Foundation Models and Graph Optimization](https://mlsanity.com/paper/2509.23991/) (2025)
- [Depth Anything in 360^circ: Towards Scale Invariance in the Wild](https://mlsanity.com/paper/2512.22819/) (2025)
- [Calibrating Panoramic Depth Estimation for Practical Localization and Mapping](https://mlsanity.com/paper/2308.14005/) (2023)
- [CasaMaestro: Multi-View Panoramas for House-Scale 3D Reconstruction](https://mlsanity.com/paper/2606.31086/) (2026)
- [360^circ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer](https://mlsanity.com/paper/2304.07967/) (2023)

## Abstract (arXiv)

We propose a novel approach to compute high-resolution (2048x1024 and higher) depths for panoramas that is significantly faster and qualitatively and qualitatively more accurate than the current state-of-the-art method (360MonoDepth). As traditional neural network-based methods have limitations in the output image sizes (up to 1024x512) due to GPU memory constraints, both 360MonoDepth and our method rely on stitching multiple perspective disparity or depth images to come out a unified panoramic depth map. However, to achieve globally consistent stitching, 360MonoDepth relied on solving extensive disparity map alignment and Poisson-based blending problems, leading to high computation time. Instead, we propose to use an existing panoramic depth map (computed in real-time by any panorama-based method) as the common target for the individual perspective depth maps to register to. This key idea made producing globally consistent stitching results from a straightforward task. Our experiments show that our method generates qualitatively better results than existing panorama-based methods, and further outperforms them quantitatively on datasets unseen by these methods.