3D Intrinsic Shape Recognition Under Deformation and View Changes

在该建议中，我们解决了新的视觉识别问题的3D（三维）可变形物体形状的身份或类别。3D物体的图像由于不同的物体姿态（关节或变形）以及相机视点而经历大的外观变化。我们试图从单个图像中识别出物体的三维形状身份（或内在形状），而不管它们目前的姿势和相机视点。人类可以从单个图像中感知物体的3D形状，前提是他们之前已经看到过类似其他物体的3D形状。以前在3D形状上学习的知识称为3D形状先验。完成该任务的一个关键思想是学习和利用物体识别的形状先验知识，该研究符合并超越了计算机视觉的重要主题。鉴于许多工作的视点不变的对象识别限于刚性对象类与邦蒂富尔纹理，我们认为可变形的对象形状。在单视图重建领域的一系列工作中，对于姿态变化下的人体形状重建已经显示出很好的效果。在3D人体姿势识别方面也取得了显著的最新成功。在这些结果的基础上，我们超越了捕捉物体识别的3D内在形状变化。预期成果将有利于相关学术领域及其现有市场，并将带来潜在的新应用，如自动监测公众肥胖和动物跟踪。

In the proposal, we tackle the novel visual recognition problem of 3D (three-dimensional) deformable object shape identities or categories. Images of 3D objects undergo large appearance changes due to different object poses (articulation or deformation) as well as camera view-points. We attempt to recognise objects from single images by their 3D shape identities (or intrinsic shapes) regardless of their present poses and camera view-points. Humans can perceive 3D shapes of objects from single images, provided that they have previously seen 3D shapes of similar other objects. The knowledge formerly learnt on 3D shapes is called 3D shape prior. A key idea for fulfilling the proposed task is to learn and exploit the shape priors for object recognition.The proposed research is well-lined with and goes beyond important topics of computer vision. Whereas much work for view-point invariant object recognition is limited to rigid object classes with bountiful textures, we consider deformable object shapes. In a series of work in the field of single view reconstruction, promising results have been shown for human body shape reconstruction under pose variations. There has also been a notable latest success in 3D human pose recognition. On the top of these results, we go beyond to capture 3D intrinsic shape variations for object recognition. The intended outcomes would benefit the relevant academic fields and their existing markets, and would also lead to potential new applications such as automatic monitoring of public obesity and animal tracking.