As a representative technology of artificial intelligence, 3D reconstruction based on deep learning can be integrated into the edge computing framework to form an intelligent edge and then realize the intelligent processing of the edge. Recently, high-resolution representation of 3D objects using multiview decomposition (MVD) architecture is a fast reconstruction method for generating objects with realistic details from a single RGB image. The results of high-resolution 3D object reconstruction are related to two aspects. On the one hand, a low-resolution reconstruction network represents a good 3D object from a single RGB image. On the other hand, a high-resolution reconstruction network maximizes fine low-resolution 3D objects. To improve these two aspects and further enhance the high-resolution reconstruction capabilities of the 3D object generation network, we study and improve the low-resolution 3D generation network and the depth map superresolution network. Eventually, we get an improved multiview decomposition (IMVD) network. First, we use a 2D image encoder with multifeature fusion (MFF) to enhance the feature extraction capability of the model. Second, a 3D decoder using an effective subpixel convolutional neural network (3D ESPCN) improves the decoding speed in the decoding stage. Moreover, we design a multiresidual dense block (MRDB) to optimize the depth map superresolution network, which allows the model to capture more object details and reduce the model parameters by approximately 25% when the number of network layers is doubled. The experimental results show that the proposed IMVD is better than the original MVD in the 3D object superresolution experiment and the high-resolution 3D reconstruction experiment of a single image.

中文翻译：

---

改进的多视图分解，用于单图像高分辨率3D对象重建

作为人工智能的代表技术，可以将基于深度学习的3D重建集成到边缘计算框架中，以形成智能边缘，然后实现边缘的智能处理。近来，使用多视图分解（MVD）架构的3D对象的高分辨率表示是一种快速重建方法，用于从单个RGB图像生成具有逼真的细节的对象。高分辨率3D对象重建的结果涉及两个方面。一方面，低分辨率的重建网络从单个RGB图像中代表了良好的3D对象。另一方面，高分辨率重建网络可最大化精细的低分辨率3D对象。为了改善这两个方面并进一步增强3D对象生成网络的高分辨率重建能力，我们研究和改进了低分辨率3D生成网络和深度图超分辨率网络。最终，我们得到了改进的多视图分解（IMVD）网络。首先，我们使用具有多特征融合（MFF）的2D图像编码器来增强模型的特征提取能力。其次，使用有效子像素卷积神经网络（3D ESPCN）的3D解码器提高了解码阶段的解码速度。此外，我们设计了一个多残差密集块（MRDB）来优化深度图超分辨率网络，当网络层数增加一倍时，该模型可以捕获更多的对象细节并将模型参数减少约25％。