Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model

Zhang, Saiping; Jin, Dongyang; Dai, Yuchao; Yang, Fuzheng

doi:10.1109/tip.2022.3144891

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…Thus, bounded coordinate transfers that offer learnable physical geometric information are realized through cross-scene learning. Relative camera pose regression [161], [162], [163], [164] techniques produce reference images. For the relative camera pose, regression may be computed using the previous image retrieval method [165], which first determines the image in the database that is most similar to the query image, and then calculates the absolute pose of the target image after predicting their respective relative poses.…”

Section: ) Relative Pose Regressionmentioning

confidence: 99%

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

et al. 2023

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Camera localization involves the estimation of the camera pose of an image from a random scene. We used a single image or sequence of images or videos as the input. The output depends on the representation of the scene and method used. Several computer vision applications, such as robot navigation and safety inspection, can benefit from camera localization. Camera localization is used to determine the position of an object on the camera in an image containing multiple images in a sequence. Structure-based localization techniques have achieved considerable success owing to a combination of image matching and coordinate regression. Absolute and relative pose regression techniques can provide end-to-end learning; however, they exhibit poor accuracies. Despite the rapid growth in computer vision, there has been no thorough review of the categorization, evaluation, and synthesis of structures and regression-based techniques. Input format and loss strategies for recurrent neural networks (RNN) have not been adequately described in the literature. The main topic is indoor camera pose regression, which is a part of the camera localization techniques. First, we discuss certain application areas for camera localization. We then discuss different camera localization techniques, such as feature and structure-based, absolute and relative pose regression techniques, and simultaneous localization and mapping (SLAM). We evaluated the frequently used datasets and qualitatively compared the absolute and relative camera pose estimation approaches. Finally, we discuss potential directions for future research, such as optimizing the computational cost of the features and evaluating the end-to-end characteristics of multiple cameras.INDEX TERMS Camera pose regression, absolute pose regression, indoor positioning, camera localization, robot navigation, SLAM.

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Section: ) Relative Pose Regressionmentioning

confidence: 99%

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

et al. 2023

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A Pig Pose Estimation Model for Measuring Pig’s Body Size

Yang¹,

Qin²,

Sun³

et al. 2022

Lecture Notes in Computer Science

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A comprehensive research on light field imaging: Theory and application

Liu,

Wang,

Yang

et al. 2024

IET Computer Vision

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Computational photography is a combination of novel optical designs and processing methods to capture high‐dimensional visual information. As an emerged promising technique, light field (LF) imaging measures the lighting, reflectance, focus, geometry and viewpoint in the free space, which has been widely explored for depth estimation, view synthesis, refocus, rendering, 3D displays, microscopy and other applications in computer vision in the past decades. In this paper, the authors present a comprehensive research survey on the LF imaging theory, technology and application. Firstly, the LF imaging process based on a MicroLens Array structure is derived, that is MLA‐LF. Subsequently, the innovations of LF imaging technology are presented in terms of the imaging prototype, consumer LF camera and LF displays in Virtual Reality (VR) and Augmented Reality (AR). Finally the applications and challenges of LF imaging integrating with deep learning models are analysed, which consist of depth estimation, saliency detection, semantic segmentation, de‐occlusion and defocus deblurring in recent years. It is believed that this paper will be a good reference for the future research on LF imaging technology in Artificial Intelligence era.

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Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model

Cited by 6 publications

References 39 publications

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

A Pig Pose Estimation Model for Measuring Pig’s Body Size

A comprehensive research on light field imaging: Theory and application

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