# PICCOLO: Point Cloud-Centric Omnidirectional Localization

arXiv: https://arxiv.org/abs/2108.06545 · HTML: https://mlsanity.com/paper/2108.06545/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017)
- [DSAC - Differentiable RANSAC for Camera Localization](https://mlsanity.com/paper/1611.05705/) (2016)
- [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)
- [Robust Image Retrieval-based Visual Localization using Kapture](https://mlsanity.com/paper/2007.13867/) (2020)
- [Learning to Navigate the Energy Landscape](https://mlsanity.com/paper/1603.05772/) (2016)
- [Benchmarking Image Retrieval for Visual Localization](https://mlsanity.com/paper/2011.11946/) (2020)
- [Beyond Controlled Environments: 3D Camera Re-Localization in Changing Indoor Scenes](https://mlsanity.com/paper/2008.02004/) (2020)
- [The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation](https://mlsanity.com/paper/1812.01232/) (2018)
- [Graph-Based Classification of Omnidirectional Images](https://mlsanity.com/paper/1707.08301/) (2017)
- [Adapting Computer Vision Algorithms for Omnidirectional Video](https://mlsanity.com/paper/1907.09233/) (2019)

## Built on by (most cited first, 14 of 14)

- [360Loc: A Dataset and Benchmark for Omnidirectional Visual Localization with Cross-device Queries](https://mlsanity.com/paper/2311.17389/) (2023)
- [ODGS: 3D Scene Reconstruction from Omnidirectional Images with 3D Gaussian Splattings](https://mlsanity.com/paper/2410.20686/) (2024)
- [Fully Geometric Panoramic Localization](https://mlsanity.com/paper/2403.19904/) (2024)
- [OmniSplat: Taming Feed-Forward 3D Gaussian Splatting for Omnidirectional Images with Editable Capabilities](https://mlsanity.com/paper/2412.16604/) (2024)
- [Privacy-Preserving Visual Localization with Event Cameras](https://mlsanity.com/paper/2212.03177/) (2022)
- [OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds](https://mlsanity.com/paper/2404.04693/) (2024)
- [ErpGS: Equirectangular Image Rendering enhanced with 3D Gaussian Regularization](https://mlsanity.com/paper/2505.19883/) (2025)
- [CPO: Change Robust Panorama to Point Cloud Localization](https://mlsanity.com/paper/2207.05317/) (2022)
- [MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents](https://mlsanity.com/paper/2211.15992/) (2022)
- [LDL: Line Distance Functions for Panoramic Localization](https://mlsanity.com/paper/2308.13989/) (2023)
- [You Need a Transition Plane: Bridging Continuous Panoramic 3D Reconstruction with Perspective Gaussian Splatting](https://mlsanity.com/paper/2504.09062/) (2025)
- [OB3D: A New Dataset for Benchmarking Omnidirectional 3D Reconstruction Using Blender](https://mlsanity.com/paper/2505.20126/) (2025)
- [Calibrating Panoramic Depth Estimation for Practical Localization and Mapping](https://mlsanity.com/paper/2308.14005/) (2023)
- [PanoSeg3R: Feed-Forward 3D Semantic Segmentation for Panoramic Images with an Automatic Data Curation Pipeline](https://mlsanity.com/paper/2609.22687/) (2026)

## Abstract (arXiv)

We present PICCOLO, a simple and efficient algorithm for omnidirectional localization. Given a colored point cloud and a 360 panorama image of a scene, our objective is to recover the camera pose at which the panorama image is taken. Our pipeline works in an off-the-shelf manner with a single image given as a query and does not require any training of neural networks or collecting ground-truth poses of images. Instead, we match each point cloud color to the holistic view of the panorama image with gradient-descent optimization to find the camera pose. Our loss function, called sampling loss, is point cloud-centric, evaluated at the projected location of every point in the point cloud. In contrast, conventional photometric loss is image-centric, comparing colors at each pixel location. With a simple change in the compared entities, sampling loss effectively overcomes the severe visual distortion of omnidirectional images, and enjoys the global context of the 360 view to handle challenging scenarios for visual localization. PICCOLO outperforms existing omnidirectional localization algorithms in both accuracy and stability when evaluated in various environments. Code is available at urlhttps://github.com/82magnolia/panoramic-localization/.