mlsanityData to 7 Oct 2026

Pose-GNN : Camera Pose Estimation System Using Graph Neural Networks

arXiv 2103.09435 ↗First posted 17 Mar 2021cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

We propose a novel image based localization system using graph neural networks (GNN). The pretrained ResNet50 convolutional neural network (CNN) architecture is used to extract the important features for each image. Following, the extracted features are input to GNN to find the pose of each image by either using the image features as a node in a graph and formulate the pose estimation problem as node pose regression or modelling the image features themselves as a graph and the problem becomes graph pose regression. We do an extensive comparison between the proposed two approaches and the state of the art single image localization methods and show that using GNN leads to enhanced performance for both indoor and outdoor environments.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 23 of its 46 references that are in the map. First posted on arXiv 17 Mar 2021.

Builds on

23 of 46 references.

References · 23

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11 more
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2.

4 papers.

  1. 2022Multi-Robot Collaborative Perception with Graph Neural Networkscited by 34
  2. 2023NeRFtrinsic Four: An End-To-End Trainable NeRF Jointly Optimizing Diverse Intrinsic and Extrinsic Camera Parameterscited by 6
  3. 2023CGA-PoseNet: Camera Pose Regression via a 1D-Up Approach to Conformal Geometric Algebracited by 2
  4. 2022S3E-GNN: Sparse Spatial Scene Embedding with Graph Neural Networks for Camera Relocalizationcited by 0