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Integration of geometric elements, Euclidean relations, and motion curves for parametric shape and motion estimation

机译:整合几何元素,欧几里得关系和运动曲线以进行参数形状和运动估计

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This paper presents an approach to shape and motion estimation that integrates heterogeneous knowledge into a unique model-based framework. We describe the observed scenes in terms of structured geometric elements (points, line segments, rectangles, 3D corners) sharing explicitly Euclidean relationships (orthogonality, parallelism, colinearity, coplanarity). Camera trajectories are represented with adaptative models which account for the regularity of usual camera motions. Two different strategies of automatic model building lead us to reduced models for shape and motion estimation with a minimal number of parameters. These models increase the robustness to noise and occlusions, improve the reconstruction, and provide a high-level representation of the observed scene. The parameters are optimally computed within a sequential Bayesian estimation procedure that gives accurate and reliable results on synthetic and real video imagery.
机译:本文提出了一种形状和运动估计的方法,该方法将异构知识集成到基于模型的独特框架中。我们用结构化的几何元素(点,线段,矩形,3D角)明确共享欧几里得关系(正交性,平行度,共线性,共面性)来描述观察到的场景。相机轨迹用自适应模型表示,该模型考虑了通常相机运动的规律性。自动建模的两种不同策略使我们可以使用最少的参数来简化形状和运动估计的模型。这些模型提高了对噪声和遮挡的鲁棒性,改进了重建,并提供了所观察场景的高级表示。这些参数是在顺序贝叶斯估计过程中最佳计算的,该过程可在合成和真实视频图像上给出准确可靠的结果。

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