Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/232543
Title: A Data-Driven Point Cloud Simplification Framework for City-Scale Image-Based Localization
Authors: Wentao Cheng;Weisi Lin;Xinfeng Zhang;Michael Goesele;Ming-Ting Sun
Year: 2017
Publisher: IEEE
Abstract: City-scale 3D point clouds reconstructed via structure-from-motion from a large collection of Internet images are widely used in the image-based localization task to estimate a 6-DOF camera pose of a query image. Due to prohibitive memory footprint of city-scale point clouds, image-based localization is difficult to be implemented on devices with limited memory resources. Point cloud simplification aims to select a subset of points to achieve a comparable localization performance using the original point cloud. In this paper, we propose a data-driven point cloud simplification framework by taking it as a weighted K-Cover problem, which mainly includes two complementary parts. First, a utility-based parameter determination method is proposed to select a reasonable parameter K for K-Cover-based approaches by evaluating the potential of a point cloud for establishing sufficient 2D-3D feature correspondences. Second, we formulate the 3D point cloud simplification problem as a weighted K-Cover problem, and propose an adaptive exponential weight function based on the visibility probability of 3D points. The experimental results on three popular datasets demonstrate that the proposed point cloud simplification framework outperforms the state-of-the-art methods for the image-based localization application with a well predicted parameter in the K-Cover problem.
URI: http://localhost/handle/Hannan/232543
volume: 26
issue: 1
More Information: 262,
275
Appears in Collections:2017

Files in This Item:
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7725940.pdf3.72 MBAdobe PDF
Title: A Data-Driven Point Cloud Simplification Framework for City-Scale Image-Based Localization
Authors: Wentao Cheng;Weisi Lin;Xinfeng Zhang;Michael Goesele;Ming-Ting Sun
Year: 2017
Publisher: IEEE
Abstract: City-scale 3D point clouds reconstructed via structure-from-motion from a large collection of Internet images are widely used in the image-based localization task to estimate a 6-DOF camera pose of a query image. Due to prohibitive memory footprint of city-scale point clouds, image-based localization is difficult to be implemented on devices with limited memory resources. Point cloud simplification aims to select a subset of points to achieve a comparable localization performance using the original point cloud. In this paper, we propose a data-driven point cloud simplification framework by taking it as a weighted K-Cover problem, which mainly includes two complementary parts. First, a utility-based parameter determination method is proposed to select a reasonable parameter K for K-Cover-based approaches by evaluating the potential of a point cloud for establishing sufficient 2D-3D feature correspondences. Second, we formulate the 3D point cloud simplification problem as a weighted K-Cover problem, and propose an adaptive exponential weight function based on the visibility probability of 3D points. The experimental results on three popular datasets demonstrate that the proposed point cloud simplification framework outperforms the state-of-the-art methods for the image-based localization application with a well predicted parameter in the K-Cover problem.
URI: http://localhost/handle/Hannan/232543
volume: 26
issue: 1
More Information: 262,
275
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7725940.pdf3.72 MBAdobe PDF
Title: A Data-Driven Point Cloud Simplification Framework for City-Scale Image-Based Localization
Authors: Wentao Cheng;Weisi Lin;Xinfeng Zhang;Michael Goesele;Ming-Ting Sun
Year: 2017
Publisher: IEEE
Abstract: City-scale 3D point clouds reconstructed via structure-from-motion from a large collection of Internet images are widely used in the image-based localization task to estimate a 6-DOF camera pose of a query image. Due to prohibitive memory footprint of city-scale point clouds, image-based localization is difficult to be implemented on devices with limited memory resources. Point cloud simplification aims to select a subset of points to achieve a comparable localization performance using the original point cloud. In this paper, we propose a data-driven point cloud simplification framework by taking it as a weighted K-Cover problem, which mainly includes two complementary parts. First, a utility-based parameter determination method is proposed to select a reasonable parameter K for K-Cover-based approaches by evaluating the potential of a point cloud for establishing sufficient 2D-3D feature correspondences. Second, we formulate the 3D point cloud simplification problem as a weighted K-Cover problem, and propose an adaptive exponential weight function based on the visibility probability of 3D points. The experimental results on three popular datasets demonstrate that the proposed point cloud simplification framework outperforms the state-of-the-art methods for the image-based localization application with a well predicted parameter in the K-Cover problem.
URI: http://localhost/handle/Hannan/232543
volume: 26
issue: 1
More Information: 262,
275
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7725940.pdf3.72 MBAdobe PDF