# 3D reconstruction from spherical images: A review of techniques, applications, and prospects arXiv: https://arxiv.org/abs/2302.04495 ยท HTML: https://mlsanity.com/paper/2302.04495/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 40 of its 164 references that are in the map. - First posted on arXiv 9 Feb 2023. ## Builds on (most cited first, 30 of 40) - [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019) - [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017) - [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017) - [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015) - [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021) - [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020) - [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016) - [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018) - [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018) - [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019) - [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018) - [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016) - [Spherical CNNs](https://mlsanity.com/paper/1801.10130/) (2018) - [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017) - [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017) - [OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas](https://mlsanity.com/paper/1807.09620/) (2018) - [StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction](https://mlsanity.com/paper/1807.08865/) (2018) - [Spherical CNNs on Unstructured Grids](https://mlsanity.com/paper/1901.02039/) (2019) - [ContextDesc: Local Descriptor Augmentation with Cross-Modality Context](https://mlsanity.com/paper/1904.04084/) (2019) - [Tangent Images for Mitigating Spherical Distortion](https://mlsanity.com/paper/1912.09390/) (2019) - [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://mlsanity.com/paper/1608.01807/) (2016) - [DeepSphere: a graph-based spherical CNN](https://mlsanity.com/paper/2012.15000/) (2020) - [OpenVSLAM: A Versatile Visual SLAM Framework](https://mlsanity.com/paper/1910.01122/) (2019) - [Kernel Transformer Networks for Compact Spherical Convolution](https://mlsanity.com/paper/1812.03115/) (2018) - [GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints](https://mlsanity.com/paper/1807.06294/) (2018) - [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022) - [Self-Supervised Learning of Depth and Camera Motion from 360deg Videos](https://mlsanity.com/paper/1811.05304/) (2018) - [360SD-Net: 360deg Stereo Depth Estimation with Learnable Cost Volume](https://mlsanity.com/paper/1911.04460/) (2019) - [Learning Local Image Descriptors with Deep Siamese and Triplet Convolutional Networks by Minimising Global Loss Functions](https://mlsanity.com/paper/1512.09272/) (2015) - [OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems](https://mlsanity.com/paper/2003.08056/) (2020) ## Built on by (most cited first, 7 of 7) - [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025) - [OmniGS: Fast Radiance Field Reconstruction using Omnidirectional Gaussian Splatting](https://mlsanity.com/paper/2404.03202/) (2024) - [Stereographic Spherical Sliced Wasserstein Distances](https://mlsanity.com/paper/2402.02345/) (2024) - [3D Reconstruction of Spherical Images based on Incremental Structure from Motion](https://mlsanity.com/paper/2306.12770/) (2023) - [IM360: Large-scale Indoor Mapping with 360 Cameras](https://mlsanity.com/paper/2502.12545/) (2025) - [Spherical Tree-Sliced Wasserstein Distance](https://mlsanity.com/paper/2503.11249/) (2025) - [SIM: A mapping framework for built environment auditing based on street view imagery](https://mlsanity.com/paper/2505.24076/) (2025) ## Abstract (arXiv) 3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one camera exposure. In contrast to perspective images with limited FOV (Field of View), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumer-grade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction of spherical images. As a result, this research provides a thorough survey of the state-of-the-art for 3D reconstruction of spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.