Learning-based objective evaluation of 3D human open meshes
Learning-based objective evaluation of 3D human open meshes
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基于学习的 3D 人体开放网格客观评估
DOI：
10.1109/icme.2017.8019470
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
B. Prabhakaran
中科院分区：
文献类型：
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作者：
Kevin Desai;K. Bahirat;B. Prabhakaran
目前最先进的网格质量措施评估经过网格后处理应用（如网格简化或水印）获得的封闭和完整网格，并将它们与相应的参考网格进行比较。新兴的3D沉浸式VR/AR应用使用嘈杂的3D点云，通常来自单个RGB-D相机（如微软的Kinect），以实时生成独立的（无参考的）3D人体开放网格（有边界），这需要评估。通过模拟人类对三维人体开放网格质量的感知，提出了一种基于学习的客观评价视觉质量的方法。进行两方面的客观评价：(a)全局整体评分，通过考虑网格完整性和网格噪声，捕获网格作为整体代表人体模型的有效性。(b)局部部分评分满足人体不同部位粗糙度变化的需要，通过寻找该部位（段）中所有相邻三角形的面法线偏差。学习技术将客观评分与主观用户评价相结合，进而将三维网格的白盒和黑盒评价的概念结合起来。实验结果证明了该方法的有效性。
Current state-of-the-art mesh quality measures evaluate closed and complete meshes obtained after mesh postprocessing applications, such as mesh simplification or watermarking, and compare them against the corresponding reference mesh. Emerging 3D immersive VR/AR applications use noisy 3D point cloud, typically from single RGB-D camera (such as Microsoft's Kinect) to generate standalone (no reference) 3D human open mesh (with boundaries) in real time, that needs evaluation. A learning-based objective measure is proposed to rate the visual quality by emulating human perception of 3D human open mesh quality. 2-pronged objective evaluation is performed: (a) Global holistic score captures the efficacy of the mesh to represent the human model as a whole, by considering mesh completeness and mesh noise. (b) Local part-based score caters to the need of varying roughness in different parts of the human body, by finding the deviation in the face normals for all the adjacent triangles in that part (segment). Learning technique aligns the objective scores with the subjective user evaluation, in turn combining the concepts of white-box and black-box evaluation for 3D meshes. Experimental results for a database, specifically generated for the purpose proves the efficacy of the proposed method.