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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation
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A Nonrigid Kernel-Based Framework for 2D-3D Pose Estimation and 2D Image Segmentation

机译：基于非硬核的2D-3D姿势估计和2D图像分割框架

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摘要

In this work, we present a nonrigid approach to jointly solving the tasks of 2D-3D pose estimation and 2D image segmentation. In general, most frameworks that couple both pose estimation and segmentation assume that one has exact knowledge of the 3D object. However, under nonideal conditions, this assumption may be violated if only a general class to which a given shape belongs is given (e.g., cars, boats, or planes). Thus, we propose to solve the 2D-3D pose estimation and 2D image segmentation via nonlinear manifold learning of 3D embedded shapes for a general class of objects or deformations for which one may not be able to associate a skeleton model. Thus, the novelty of our method is threefold: First, we present and derive a gradient flow for the task of nonrigid pose estimation and segmentation. Second, due to the possible nonlinear structures of one's training set, we evolve the preimage obtained through kernel PCA for the task of shape analysis. Third, we show that the derivation for shape weights is general. This allows us to use various kernels, as well as other statistical learning methodologies, with only minimal changes needing to be made to the overall shape evolution scheme. In contrast with other techniques, we approach the nonrigid problem, which is an infinite-dimensional task, with a finite-dimensional optimization scheme. More importantly, we do not explicitly need to know the interaction between various shapes such as that needed for skeleton models as this is done implicitly through shape learning. We provide experimental results on several challenging pose estimation and segmentation scenarios.
机译：在这项工作中，我们提出了一种非刚性方法来共同解决2D-3D姿态估计和2D图像分割的任务。通常，大多数结合了姿态估计和分割的框架都假定人们对3D对象有确切的了解。但是，在非理想条件下，如果仅给出给定形状所属的一般类别（例如汽车，轮船或飞机），则可能会违反此假设。因此，我们建议针对一类可能无法关联骨架模型的一般物体或变形，通过非线性3D嵌入形状的非线性流形学习来解决2D-3D姿态估计和2D图像分割问题。因此，我们方法的新颖性是三方面的：首先，我们提出并导出用于非刚性姿势估计和分割任务的梯度流。其次，由于一个人的训练集可能具有非线性结构，我们将通过核PCA获得的原像进行演化，以进行形状分析。第三，我们表明形状权重的推导是通用的。这使我们能够使用各种内核以及其他统计学习方法，而只需对整体形状演化方案进行最小的更改。与其他技术相比，我们采用有限维优化方案来处理非刚性问题，这是一个无限维任务。更重要的是，我们不需要明确知道各种形状之间的相互作用，例如骨架模型所需的相互作用，因为这是通过形状学习隐式完成的。我们提供了一些具有挑战性的姿势估计和分割方案的实验结果。

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