Camera Localization
38 papers with code • 2 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Camera Localization
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Aachen Day-Night
12 Scenes
HPS
CrossLoc Benchmark Datasets
Latest papers with no code
From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration
We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration.
Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds
To overcome this limitation, we focus on correlating the information of 360 equirectangular images to point clouds, proposing an end-to-end learnable network to conduct cross-modal visual localization by establishing similarity in high-dimensional feature space.
A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition
We designed RepVGG-lite as the backbone network in our architecture, it is more discriminative than other general networks in the Place Recognition task.
Uncertainty-Aware DNN for Multi-Modal Camera Localization
In the literature, uncertainty estimation in Deep Neural Networks (DNNs) is often performed through sampling methods, such as Monte Carlo Dropout (MCD) and Deep Ensemble (DE), at the expense of undesirable execution time or an increase in hardware resources.
CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization
The critical challenge of this task is to learn a powerful global feature descriptor for the sequential ground-view images while considering its domain alignment with reference satellite images.
Deep Camera Pose Regression Using Pseudo-LiDAR
By proving the validity of using pseudo-LiDAR signals over depth maps for localization, there are new considerations when implementing large-scale localization systems.
A Critical Analysis of Image-based Camera Pose Estimation Techniques
Camera, and associated with its objects within the field of view, localization could benefit many computer vision fields, such as autonomous driving, robot navigation, and augmented reality (AR).
Learning To Detect Scene Landmarks for Camera Localization
Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible.
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors.
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather
As a result, it learns to generate minimal image perturbations that are still capable of perplexing the network.
