# Cascaded Parallel Filtering for Memory-Efficient Image-Based Localization

arXiv: https://arxiv.org/abs/1908.06141 · HTML: https://mlsanity.com/paper/1908.06141/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2019.

## Built on by (most cited first, 17 of 17)

- [Is Geometry Enough for Matching in Visual Localization?](https://mlsanity.com/paper/2203.12979/) (2022)
- [Learning Camera Localization via Dense Scene Matching](https://mlsanity.com/paper/2103.16792/) (2021)
- [SFD2: Semantic-guided Feature Detection and Description](https://mlsanity.com/paper/2304.14845/) (2023)
- [NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization](https://mlsanity.com/paper/2211.11177/) (2022)
- [PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields](https://mlsanity.com/paper/2312.10649/) (2023)
- [R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization](https://mlsanity.com/paper/2501.01421/) (2025)
- [GSplatLoc: Grounding Keypoint Descriptors into 3D Gaussian Splatting for Improved Visual Localization](https://mlsanity.com/paper/2409.16502/) (2024)
- [SACReg: Scene-Agnostic Coordinate Regression for Visual Localization](https://mlsanity.com/paper/2307.11702/) (2023)
- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Long-term Visual Localization with Mobile Sensors](https://mlsanity.com/paper/2304.07691/) (2023)
- [Learning to Produce Semi-dense Correspondences for Visual Localization](https://mlsanity.com/paper/2402.08359/) (2024)
- [Differentiable Product Quantization for Memory Efficient Camera Relocalization](https://mlsanity.com/paper/2407.15540/) (2024)
- [PseudoCal: Towards Initialisation-Free Deep Learning-Based Camera-LiDAR Self-Calibration](https://mlsanity.com/paper/2309.09855/) (2023)
- [Efficient Map Sparsification Based on 2D and 3D Discretized Grids](https://mlsanity.com/paper/2303.10882/) (2023)
- [3DoF Localization from a Single Image and an Object Map: the Flatlandia Problem and Dataset](https://mlsanity.com/paper/2304.06373/) (2023)
- [PRAM: Place Recognition Anywhere Model for Efficient Visual Localization](https://mlsanity.com/paper/2404.07785/) (2024)
- [Robust Scene Coordinate Regression via Geometrically-Consistent Global Descriptors](https://mlsanity.com/paper/2512.17226/) (2025)

## Abstract (arXiv)

Image-based localization (IBL) aims to estimate the 6DOF camera pose for a given query image. The camera pose can be computed from 2D-3D matches between a query image and Structure-from-Motion (SfM) models. Despite recent advances in IBL, it remains difficult to simultaneously resolve the memory consumption and match ambiguity problems of large SfM models. In this work, we propose a cascaded parallel filtering method that leverages the feature, visibility and geometry information to filter wrong matches under binary feature representation. The core idea is that we divide the challenging filtering task into two parallel tasks before deriving an auxiliary camera pose for final filtering. One task focuses on preserving potentially correct matches, while another focuses on obtaining high quality matches to facilitate subsequent more powerful filtering. Moreover, our proposed method improves the localization accuracy by introducing a quality-aware spatial reconfiguration method and a principal focal length enhanced pose estimation method. Experimental results on real-world datasets demonstrate that our method achieves very competitive localization performances in a memory-efficient manner.