Schindler Konrad, Suter David
Electrical and Computer Systems Engineering Department, Monash University, Clayton Campus, Wellington Road, 3800 VIC, Australia.
IEEE Trans Pattern Anal Mach Intell. 2006 Jun;28(6):983-95. doi: 10.1109/TPAMI.2006.130.
Multibody structure-and-motion (MSaM) is the problem to establish the multiple-view geometry of several views of a 3D scene taken at different times, where the scene consists of multiple rigid objects moving relative to each other. We examine the case of two views. The setting is the following: Given are a set of corresponding image points in two images, which originate from an unknown number of moving scene objects, each giving rise to a motion model. Furthermore, the measurement noise is unknown, and there are a number of gross errors, which are outliers to all models. The task is to find an optimal set of motion models for the measurements. It is solved through Monte-Carlo sampling, careful statistical analysis of the sampled set of motion models, and simultaneous selection of multiple motion models to best explain the measurements. The framework is not restricted to any particular model selection mechanism because it is developed from a Bayesian viewpoint: Different model selection criteria are seen as different priors for the set of moving objects, which allow one to bias the selection procedure for different purposes.
多体结构与运动（MSaM）问题是要建立在不同时间拍摄的三维场景的多个视图的多视图几何关系，其中场景由多个相对彼此运动的刚性物体组成。我们研究双视图的情况。设定如下：给定两幅图像中的一组对应图像点，这些点源自数量未知的运动场景物体，每个物体都产生一个运动模型。此外，测量噪声未知，并且存在一些粗大误差，这些误差是所有模型的异常值。任务是为测量找到一组最优的运动模型。通过蒙特卡罗采样、对采样的运动模型集进行仔细的统计分析以及同时选择多个运动模型以最佳地解释测量结果来解决该问题。该框架不限于任何特定的模型选择机制，因为它是从贝叶斯观点发展而来的：不同的模型选择标准被视为运动物体集的不同先验，这允许人们为不同目的使选择过程产生偏差。
