# 3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning

arXiv: https://arxiv.org/abs/2005.06147 · HTML: https://mlsanity.com/paper/2005.06147/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 5 of 5)

- [Deep Learning for Visual Localization and Mapping: A Survey](https://mlsanity.com/paper/2308.14039/) (2023)
- [Fusing Convolutional Neural Network and Geometric Constraint for Image-based Indoor Localization](https://mlsanity.com/paper/2201.01408/) (2022)
- [AirLoc: Object-based Indoor Relocalization](https://mlsanity.com/paper/2304.00954/) (2023)
- [DynaWeightPnP: Toward global real-time 3D-2D solver in PnP without correspondences](https://mlsanity.com/paper/2409.18457/) (2024)
- [Deep auxiliary learning for visual localization using colorization task](https://mlsanity.com/paper/2107.00222/) (2021)

## Abstract (arXiv)

Camera localization is a fundamental and key component of autonomous driving vehicles and mobile robots to localize themselves globally for further environment perception, path planning and motion control. Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods. In this work, we propose a compact network for absolute camera pose regression. Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents. We add this constraint as a regularization term to our proposed network by defining a pixel-level photometric loss and an image-level structural similarity loss. To benchmark our method, different challenging scenes including indoor and outdoor environment are tested with our proposed approach and state-of-the-arts. And the experimental results demonstrate significant performance improvement of our method on both prediction accuracy and convergence efficiency.