A Critical Analysis of Image-based Camera Pose Estimation Techniques

Xu, Meng; Wang, Youchen; Xu, Bin; Zhang, Jun; Ren, Jing; Poslad, Stefan; Xu, Ping

doi:10.48550/arxiv.2201.05816

Cited by 3 publications

(3 citation statements)

References 98 publications

(130 reference statements)

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“…DH3D [34] uses an embedding of detection and description modules in a Siamese network. (3) The describe-to-detect methods extract descriptors first and then detect keypoints. D2-Net [33] detects keypoints on a dense feature map for more stable detectors, while DELF [35] is proposed for training keypoints in a local maxima way.…”

Section: Localization With Sparse Local Feature Matchingmentioning

confidence: 99%

“…In recent years, the development of deep learning and computer vision technologies [1][2][3] has led to widespread research on camera pose estimation in both academia and industry [4][5][6]. Accurate and robust camera pose estimation is crucial for downstream tasks, such as object localization, size estimation, camera movement justification, activity recognition, and more, which can enable the development of smart living spaces.…”

Section: Introduction 1background and Introductionmentioning

confidence: 99%

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Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Zhang

Gong

et al. 2023

Sensors

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Accurate and robust camera pose estimation is essential for high-level applications such as augmented reality and autonomous driving. Despite the development of global feature-based camera pose regression methods and local feature-based matching guided pose estimation methods, challenging conditions, such as illumination changes and viewpoint changes, as well as inaccurate keypoint localization, continue to affect the performance of camera pose estimation. In this paper, we propose a novel relative camera pose regression framework that uses global features with rotation consistency and local features with rotation invariance. First, we apply a multi-level deformable network to detect and describe local features, which can learn appearances and gradient information sensitive to rotation variants. Second, we process the detection and description processes using the results from pixel correspondences of the input image pairs. Finally, we propose a novel loss that combines relative regression loss and absolute regression loss, incorporating global features with geometric constraints to optimize the pose estimation model. Our extensive experiments report satisfactory accuracy on the 7Scenes dataset with an average mean translation error of 0.18 m and a rotation error of 7.44° using image pairs as input. Ablation studies were also conducted to verify the effectiveness of the proposed method in the tasks of pose estimation and image matching using the 7Scenes and HPatches datasets.

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Section: Localization With Sparse Local Feature Matchingmentioning

confidence: 99%

Section: Introduction 1background and Introductionmentioning

confidence: 99%

Regression-Based Camera Pose Estimation through Multi-Level Local Features and Global Features

Zhang

Gong

et al. 2023

Sensors

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“…Additionally, due to their low memory footprint, APRs can be deployed as a standalone application on edge devices with limited computational resources. For a survey of visual camera pose localization, refer to [44,30].…”

Section: Introductionmentioning

confidence: 99%

HyperPose: Camera Pose Localization using Attention Hypernetworks

Ferens¹,

Keller²

2023

Preprint

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In this study, we propose the use of attention hypernetworks in camera pose localization. The dynamic nature of natural scenes, including changes in environment, perspective, and lighting, creates an inherent domain gap between the training and test sets that limits the accuracy of contemporary localization networks. To overcome this issue, we suggest a camera pose regressor that integrates a hypernetwork. During inference, the hypernetwork generates adaptive weights for the localization regression heads based on the input image, effectively reducing the domain gap. We also suggest the use of a Transformer-Encoder as the hypernetwork, instead of the common multilayer perceptron, to derive an attention hypernetwork. The proposed approach achieves superior results compared to state-of-the-art methods on contemporary datasets. To the best of our knowledge, this is the first instance of using hypernetworks in camera pose regression, as well as using Transformer-Encoders as hypernetworks. We make our code publicly available 1 .

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