Geometric Loss Functions for Camera Pose Regression with Deep Learning
Abstract
Deep learning has shown to be effective for robust and real-time monocular image relocalisation. In particular, PoseNet [22] is a deep convolutional neural network which learns to regress the 6-DOF camera pose from a single image. It learns to localize using high level features and is robust to difficult lighting, motion blur and unknown camera intrinsics, where point based SIFT registration fails. However, it was trained using a naive loss function, with hyper-parameters which require expensive tuning. In this paper, we give the problem a more fundamental theoretical treatment. We explore a number of novel loss functions for learning camera pose which are based on geometry and scene reprojection error. Additionally we show how to automatically learn an optimal weighting to simultaneously regress position and orientation. By leveraging geometry, we demonstrate that our technique significantly improves PoseNets performance across datasets ranging from indoor rooms to a small city.
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Expert Sample Consensus Applied to Camera Re-Localization
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Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?
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“In the final step, the estimated pose is further refined by optimising a geometrical loss function [87] such as the keypoint reprojection error [23].”
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SOLD 2 : Self-supervised Occlusion-aware Line Description and Detection
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Multi-Frame Self-Supervised Depth with Transformers
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End-to-End Learnable Geometric Vision by Backpropagating PnP Optimization
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Accelerated Coordinate Encoding: Learning to Relocalize in Minutes Using RGB and Poses
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To Learn or Not to Learn: Visual Localization from Essential Matrices
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Deep learning methods for fingerprint-based indoor positioning: a review
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InLoc: Indoor Visual Localization with Dense Matching and View Synthesis
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CMRNet: Camera to LiDAR-Map Registration
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SANet: Scene Agnostic Network for Camera Localization
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Deep Learning for Visual Localization and Mapping: A Survey
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LaMAR: Benchmarking Localization and Mapping for Augmented Reality
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Real-Time 6DOF Pose Relocalization for Event Cameras With Stacked Spatial LSTM Networks
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6D Object Pose Regression via Supervised Learning on Point Clouds
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Hierarchical Kinematic Human Mesh Recovery
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DFNet: Enhance Absolute Pose Regression with Direct Feature Matching
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V-Eye: A Vision-Based Navigation System for the Visually Impaired
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Large-scale, real-time visual–inertial localization revisited
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Real-Time RGB-D Camera Pose Estimation in Novel Scenes Using a Relocalisation Cascade
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Geomstats: A Python Package for Riemannian Geometry in Machine Learning
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Learning Multi-View Camera Relocalization With Graph Neural Networks
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Scalable Place Recognition Under Appearance Change for Autonomous Driving
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Direct-PoseNet: Absolute Pose Regression with Photometric Consistency
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MeshLoc: Mesh-Based Visual Localization
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Hybrid Scene Compression for Visual Localization
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- Authors
- Kendall, A., & Cipolla, R.
- Affiliations
- University of Cambridge (GB)
- ORCID
- Cipolla, Roberto
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 6555-6564
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2605111497
- Cited by
- 803
- References
- 59
- Citations by year
- 2026: 28 · 2025: 67 · 2024: 79 · 2023: 98 · 2022: 94 · 2021: 115 · 2020: 133 · 2019: 116 · 2018: 61 · 2017: 12
- FWCI
- 154.16
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- PoseNet, reprojection error, loss function design, 6D pose estimation, outdoor localization
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