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A Framework for Modeling 3D Scenes Using Pose-Free Equations
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AI
DOI：10.1145/1640443.1640450.png)
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摘要
En 中文
Many applications in computer graphics require detailed 3D digital models of real-world environments. The automatic and semi-automatic modeling of such spaces presents several fundamental challenges. In this work, we present an easy and robust camera-based acquisition approach for the modeling of 3D scenes which is a significant departure from current methods. Our approach uses a novel pose-free formulation for 3D reconstruction. Unlike self-calibration, omitting pose parameters from the acquisition process implies no external calibration data must be computed or provided. This serves to significantly simplify acquisition, to fundamentally improve the robustness and accuracy of the geometric reconstruction given noise in the measurements or error in the initial estimates, and to allow using uncalibrated active correspondence methods to obtain robust data. Aside from freely taking pictures and moving an uncalibrated digital projector, scene acquisition and scene point reconstruction is automatic and requires pictures from only a few viewpoints. We demonstrate how the combination of these benefits has enabled us to acquire several large and detailed models ranging from 0.28 to 2.5 million texture-mapped triangles.
Keyword:
Algorithms
Performance
Theory
Verification
Modeling
acquisition
image-based
computer graphics
modeling
acquisition
image-based rendering
pose-free
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4.7K
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3.6W
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