论文标题
CD $^2 $：用两次倒角距离的细粒3D网状重建
CD$^2$: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance
论文作者
论文摘要
单眼3D重建是从单个RGB图像中重建对象的形状及其其他信息。在3D重建中，具有详细的表面信息和低计算成本的多边形网格是从深度学习模型中获得的最普遍的表达形式。但是，最先进的方案无法直接生成结构良好的网格，我们确定大多数网格都有严重的顶点聚类（VC）和非法扭曲（IT）问题。通过分析网格变形过程，我们指出的是，倒角距离（CD）损失的不适当使用是VC的根本原因，IT在深度学习模型中是问题。在本文中，我们最初证明了通过视觉示例和定量分析引起的CD丢失引起的这两个问题。然后，我们通过两次使用倒角距离来执行合理和自适应变形，提出了一种细粒重建方法CD $^2 $。在两个3D数据集上进行了广泛的实验，并与五个最新方案进行了比较表明，我们的CD $^2 $直接生成结构良好的网格，并且在几个定量指标方面超越了其他网格。
Monocular 3D reconstruction is to reconstruct the shape of object and its other information from a single RGB image. In 3D reconstruction, polygon mesh, with detailed surface information and low computational cost, is the most prevalent expression form obtained from deep learning models. However, the state-of-the-art schemes fail to directly generate well-structured meshes, and we identify that most meshes have severe Vertices Clustering (VC) and Illegal Twist (IT) problems. By analyzing the mesh deformation process, we pinpoint that the inappropriate usage of Chamfer Distance (CD) loss is a root cause of VC and IT problems in deep learning model. In this paper, we initially demonstrate these two problems induced by CD loss with visual examples and quantitative analyses. Then, we propose a fine-grained reconstruction method CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive deformation. Extensive experiments on two 3D datasets and comparisons with five latest schemes demonstrate that our CD$^2$ directly generates a well-structured mesh and outperforms others in terms of several quantitative metrics.
