# Geometry Depth Consistency in RGBD Relative Pose Estimation

arXiv: https://arxiv.org/abs/2401.00639 · HTML: https://mlsanity.com/paper/2401.00639/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 1 Jan 2024.

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

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [Learning Two-View Correspondences and Geometry Using Order-Aware Network](https://mlsanity.com/paper/1908.04964/) (2019)
- [GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction](https://mlsanity.com/paper/2309.02436/) (2023)
- [Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion](https://mlsanity.com/paper/1901.00063/) (2018)
- [Learnable Motion Coherence for Correspondence Pruning](https://mlsanity.com/paper/2011.14563/) (2020)
- [Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting](https://mlsanity.com/paper/2304.00467/) (2023)
- [Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras](https://mlsanity.com/paper/2207.06058/) (2022)
- [Canny-VO: Visual Odometry with RGB-D Cameras based on Geometric 3D-2D Edge Alignment](https://mlsanity.com/paper/2012.08228/) (2020)
- [Trifocal Relative Pose from Lines at Points and its Efficient Solution](https://mlsanity.com/paper/1903.09755/) (2019)
- [Continuous Direct Sparse Visual Odometry from RGB-D Images](https://mlsanity.com/paper/1904.02266/) (2019)
- [Self-Supervised Correspondence Estimation via Multiview Registration](https://mlsanity.com/paper/2212.03236/) (2022)
- [Adaptive Continuous Visual Odometry from RGB-D Images](https://mlsanity.com/paper/1910.00713/) (2019)
- [Edge-Direct Visual Odometry](https://mlsanity.com/paper/1906.04838/) (2019)
- [Benchmarking Pedestrian Odometry: The Brown Pedestrian Odometry Dataset (BPOD)](https://mlsanity.com/paper/2112.13018/) (2021)

## Abstract (arXiv)

Relative pose estimation for RGBD cameras is crucial in a number of applications. Previous approaches either rely on the RGB aspect of the images to estimate pose thus not fully making use of depth in the estimation process or estimate pose from the 3D cloud of points that each image produces, thus not making full use of RGB information. This paper shows that if one pair of correspondences is hypothesized from the RGB-based ranked-ordered correspondence list, then the space of remaining correspondences is restricted to corresponding pairs of curves nested around the hypothesized correspondence, implicitly capturing depth consistency. This simple Geometric Depth Constraint (GDC) significantly reduces potential matches. In effect this becomes a filter on possible correspondences that helps reduce the number of outliers and thus expedites RANSAC significantly. As such, the same budget of time allows for more RANSAC iterations and therefore additional robustness and a significant speedup. In addition, the paper proposed a Nested RANSAC approach that also speeds up the process, as shown through experiments on TUM, ICL-NUIM, and RGBD Scenes v2 datasets.