论文标题
来自2D图像的3D形状重建，该图像具有分离的属性流量
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
论文作者
论文摘要
从单个2D图像重建3D形状是一项具有挑战性的任务，它需要根据2D图像的语义属性估算详细的3D结构。到目前为止，以前的大多数方法仍然难以为3D重建任务提取语义属性。由于单个图像的语义属性通常是隐式和纠缠在一起的，因此以输入图像表示的详细语义结构重建3D形状仍然具有挑战性。为了解决这个问题，我们建议通过输入图像中的不同语义级别解开3DATTRIFLOW并提取语义属性。这些分离的语义属性将集成到3D形状重建过程中，该过程可以为3D形状的特定属性重建提供明确的指导。结果，3D解码器可以在网络底部明确捕获高级语义特征，并利用网络顶部的低级功能，从而可以重建更准确的3D形状。请注意，在没有额外的标签的情况下学习了明确的解开，在我们训练中使用的唯一监督是输入图像及其相应的3D形状。我们在Shapenet数据集上进行的全面实验表明，3Dattriflow优于最先进的形状重建方法，我们还验证了其在形状完成任务上的通用能力。
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, most of the previous methods still struggle to extract semantic attributes for 3D reconstruction task. Since the semantic attributes of a single image are usually implicit and entangled with each other, it is still challenging to reconstruct 3D shape with detailed semantic structures represented by the input image. To address this problem, we propose 3DAttriFlow to disentangle and extract semantic attributes through different semantic levels in the input images. These disentangled semantic attributes will be integrated into the 3D shape reconstruction process, which can provide definite guidance to the reconstruction of specific attribute on 3D shape. As a result, the 3D decoder can explicitly capture high-level semantic features at the bottom of the network, and utilize low-level features at the top of the network, which allows to reconstruct more accurate 3D shapes. Note that the explicit disentangling is learned without extra labels, where the only supervision used in our training is the input image and its corresponding 3D shape. Our comprehensive experiments on ShapeNet dataset demonstrate that 3DAttriFlow outperforms the state-of-the-art shape reconstruction methods, and we also validate its generalization ability on shape completion task.
