三维世界中的变化检测

Thomas B. Pollard, J. Mundy
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引用次数: 128

摘要

本文研究了从一系列图像中检测三维场景变化的问题,这些图像是由相机以任意但已知的姿势拍摄的。不需要预先知道物体表面的正常外观和几何形状,因此序列的任何图像都可能发生异常变化。据作者所知,这篇论文是第一个在这样一个通用框架下解决变更检测问题的。现有的利用多视点图像的变化检测算法通常只能检测运动变化或假设一个平面世界几何形状,不能有效地处理由于遮挡和未建模的三维场景几何形状(自我运动视差)而导致的外观变化。这里提出的方法可以通过维护一个基于三维体素的模型来管理未知和有时变化的世界表面的复杂性,其中表面占用和图像外观的概率分布存储在每个体素中。当接收到新图像时,每个体素的概率分布会不断更新。这个联合估计问题的收敛性的关键问题是由一个基于真实世界场景性质的现实假设的形式化证明来回答的。给出了在实验室控制条件和空中侦察场景下评估变化检测精度的一系列实验
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Change Detection in a 3-d World
This paper examines the problem of detecting changes in a 3-d scene from a sequence of images, taken by cameras with arbitrary but known pose. No prior knowledge of the state of normal appearance and geometry of object surfaces is assumed, and abnormal changes can occur in any image of the sequence. To the authors' knowledge, this paper is the first to address the change detection problem in such a general framework. Existing change detection algorithms that exploit multiple image viewpoints typically can detect only motion changes or assume a planar world geometry which cannot cope effectively with appearance changes due to occlusion and un-modeled 3-d scene geometry (ego-motion parallax). The approach presented here can manage the complications of unknown and sometimes changing world surfaces by maintaining a 3-d voxel-based model, where probability distributions for surface occupancy and image appearance are stored in each voxel. The probability distributions at each voxel are continuously updated as new images are received. The key question of convergence of this joint estimation problem is answered by a formal proof based on realistic assumptions about the nature of real world scenes. A series of experiments are presented that evaluate change detection accuracy under laboratory-controlled conditions as well as aerial reconnaissance scenarios.-
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