A Field Model for Repairing 3D Shapes
A Field Model for Repairing 3D Shapes
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DOI：
10.1109/cvpr.2016.612
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Nguyen;Binh-Son Hua;Minh-Khoi Tran;Quang-Hieu Pham;Sai-Kit Yeung
中科院分区：
文献类型：
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作者：
D. Nguyen;Binh-Son Hua;Minh-Khoi Tran;Quang-Hieu Pham;Sai-Kit Yeung
提出了一种基于多视点RGB数据的三维形状修复场模型。具体来说，我们表示在马尔可夫随机场（MRF）中的3D形状，其中的几何信息是由随机二进制变量编码和外观信息是从一组在多个视点捕获的RGB图像检索。MRF模型中的局部先验捕捉对象形状的局部结构，并使用卷积深度信念网络从3D形状模板中学习。在相应的MRF中，将3D形状的修复公式化为最大后验概率（MAP）估计。采用变分平均场近似技术进行MAP估计。在人工数据和实际场景重建得到的真实的数据上对该方法进行了评价。实验结果表明，该方法在修复噪声和不完整的三维形状的鲁棒性和效率。
This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori (MAP) estimation in the corresponding MRF. Variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes.