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Found 10 papers, 4 papers with code
Coarse-to-Fine Multi-Scene Pose Regression with Transformers
Absolute camera pose regressors estimate the position and orientation of a camera given the captured image alone.
Learning to Localize in Unseen Scenes with Relative Pose Regressors
In practice, however, the performance of RPRs is significantly degraded in unseen scenes.
Camera Pose Auto-Encoders for Improving Pose Regression
In this work, we introduce Camera Pose Auto-Encoders (PAEs), multilayer perceptrons that are trained via a Teacher-Student approach to encode camera poses using APRs as their teachers.
WT-MVSNet: Window-based Transformers for Multi-view Stereo
In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo.
Adversarial Learning of Hard Positives for Place Recognition
Our method learns local and global augmentation policies which will increase the training loss, while the image retrieval network is forced to learn more powerful features for discriminating increasingly difficult examples.
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching
Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching.
Paying Attention to Activation Maps in Camera Pose Regression
Transformers are applied to encode the sequential activation maps as latent vectors, used for camera pose regression.
Learning Multi-Scene Absolute Pose Regression with Transformers
Absolute camera pose regressors estimate the position and orientation of a camera from the captured image alone.
Do We Really Need Scene-specific Pose Encoders?
In this work, we propose that scene-specific pose encoders are not required for pose regression and that encodings trained for visual similarity can be used instead.
Introduction to Camera Pose Estimation with Deep Learning
Recently this idea was applied for regressing the absolute camera pose from an RGB image.
