# Large-scale Localization Datasets in Crowded Indoor Spaces

arXiv: https://arxiv.org/abs/2105.08941 · HTML: https://mlsanity.com/paper/2105.08941/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 66 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 22 of 22)

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [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)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [Learning with Average Precision: Training Image Retrieval with a Listwise Loss](https://mlsanity.com/paper/1906.07589/) (2019)
- [Understanding the Limitations of CNN-based Absolute Camera Pose Regression](https://mlsanity.com/paper/1903.07504/) (2019)
- [A Survey of Structure from Motion](https://mlsanity.com/paper/1701.08493/) (2017)
- [Learning Less is More - 6D Camera Localization via 3D Surface Regression](https://mlsanity.com/paper/1711.10228/) (2017)
- [Image-based localization using LSTMs for structured feature correlation](https://mlsanity.com/paper/1611.07890/) (2016)
- [Hierarchical Scene Coordinate Classification and Regression for Visual Localization](https://mlsanity.com/paper/1909.06216/) (2019)
- [Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network](https://mlsanity.com/paper/1707.09733/) (2017)
- [Expert Sample Consensus Applied to Camera Re-Localization](https://mlsanity.com/paper/1908.02484/) (2019)
- [Robust Image Retrieval-based Visual Localization using Kapture](https://mlsanity.com/paper/2007.13867/) (2020)
- [Continuous-Time Visual-Inertial Odometry for Event Cameras](https://mlsanity.com/paper/1702.07389/) (2017)
- [Benchmarking Image Retrieval for Visual Localization](https://mlsanity.com/paper/2011.11946/) (2020)
- [Random Forests versus Neural Networks - What's Best for Camera Localization?](https://mlsanity.com/paper/1609.05797/) (2016)
- [Unifying Deep Local and Global Features for Image Search](https://mlsanity.com/paper/2001.05027/) (2020)
- [Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis](https://mlsanity.com/paper/2005.05179/) (2020)

## Built on by (most cited first, 29 of 29)

- [CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical Flow](https://mlsanity.com/paper/2211.10408/) (2022)
- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation](https://mlsanity.com/paper/2404.06609/) (2024)
- [360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries](https://mlsanity.com/paper/2311.17389/) (2023)
- [Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark](https://mlsanity.com/paper/2205.15761/) (2022)
- [NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization](https://mlsanity.com/paper/2211.11177/) (2022)
- [A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments](https://mlsanity.com/paper/2203.06612/) (2022)
- [R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization](https://mlsanity.com/paper/2501.01421/) (2025)
- [A Guide to Structureless Visual Localization](https://mlsanity.com/paper/2504.17636/) (2025)
- [Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset](https://mlsanity.com/paper/2506.04224/) (2025)
- [HPointLoc: Point-based Indoor Place Recognition using Synthetic RGB-D Images](https://mlsanity.com/paper/2212.14649/) (2022)
- [DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes](https://mlsanity.com/paper/2108.05615/) (2021)
- [OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds](https://mlsanity.com/paper/2404.04693/) (2024)
- [Benchmarking Visual Localization for Autonomous Navigation](https://mlsanity.com/paper/2203.13048/) (2022)
- [Combining Absolute and Semi-Generalized Relative Poses for Visual Localization](https://mlsanity.com/paper/2409.14269/) (2024)
- [Reasoning in visual navigation of end-to-end trained agents: a dynamical systems approach](https://mlsanity.com/paper/2503.08306/) (2025)
- [Reprojection Errors as Prompts for Efficient Scene Coordinate Regression](https://mlsanity.com/paper/2409.04178/) (2024)
- [IM360: Large-scale Indoor Mapping with 360 Cameras](https://mlsanity.com/paper/2502.12545/) (2025)
- [MMS-VPR: A Fine-Grained Multimodal Street-Level Visual Place Recognition Dataset and Evaluation Benchmark for Dense Pedestrian Environments](https://mlsanity.com/paper/2505.12254/) (2025)
- [TextInPlace: Indoor Visual Place Recognition in Repetitive Structures with Scene Text Spotting and Verification](https://mlsanity.com/paper/2503.06501/) (2025)
- [Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency](https://mlsanity.com/paper/2402.09237/) (2024)
- [Reference-Induced Consensus for Selective Posed-Reference Visual Localization](https://mlsanity.com/paper/2607.04722/) (2026)
- [Synfeal: A Data-Driven Simulator for End-to-End Camera Localization](https://mlsanity.com/paper/2305.18260/) (2023)
- [Distillation Improves Visual Place Recognition for Low Quality Images](https://mlsanity.com/paper/2310.06906/) (2023)
- [NVSim: Novel View Synthesis Simulator for Large Scale Indoor Navigation](https://mlsanity.com/paper/2510.24335/) (2025)
- [Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors](https://mlsanity.com/paper/2512.17226/) (2025)
- [Seeing Through the Weights: Privacy Leakage in Scene Coordinate Regression](https://mlsanity.com/paper/2606.31164/) (2026)
- [Accuracy potential of visual localization exploiting high-end street-level imagery](https://mlsanity.com/paper/2607.24409/) (2026)
- [Recovering from Pose-Selection Failures in Repeated Visual Localization](https://mlsanity.com/paper/2610.16409/) (2026)

## Abstract (arXiv)

Estimating the precise location of a camera using visual localization enables interesting applications such as augmented reality or robot navigation. This is particularly useful in indoor environments where other localization technologies, such as GNSS, fail. Indoor spaces impose interesting challenges on visual localization algorithms: occlusions due to people, textureless surfaces, large viewpoint changes, low light, repetitive textures, etc. Existing indoor datasets are either comparably small or do only cover a subset of the mentioned challenges. In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments. They were captured in a large shopping mall and a large metro station in Seoul, South Korea, using a dedicated mapping platform consisting of 10 cameras and 2 laser scanners. In order to obtain accurate ground truth camera poses, we developed a robust LiDAR SLAM which provides initial poses that are then refined using a novel structure-from-motion based optimization. We present a benchmark of modern visual localization algorithms on these challenging datasets showing superior performance of structure-based methods using robust image features. The datasets are available at: https://naverlabs.com/datasets