论文标题
GFPNET：通用拟合原语中学习形状完成的深层网络
GFPNet: A Deep Network for Learning Shape Completion in Generic Fitted Primitives
论文作者
论文摘要
在本文中，我们提出了一种对象重建设备，该设备使用所谓的通用原语（GP）完成形状。 GP是一个3D点云，描绘了一类对象的广义形状。为了重建场景中的对象，我们首先将GP贴在每个遮挡的对象上，以获得初始的原始结构。其次，我们使用基于模型的变形技术将GP的表面折叠在封闭的对象上。变形模型在深神网络（DNN）的层中编码。网络的目的是将对象的特殊性从场景转移到由GP表示的原始体积。我们表明，GFPNET通过在ModelNet和Kitti基准测试数据集上提供性能结果来与最先进的形状完成方法竞争。
In this paper, we propose an object reconstruction apparatus that uses the so-called Generic Primitives (GP) to complete shapes. A GP is a 3D point cloud depicting a generalized shape of a class of objects. To reconstruct the objects in a scene we first fit a GP onto each occluded object to obtain an initial raw structure. Secondly, we use a model-based deformation technique to fold the surface of the GP over the occluded object. The deformation model is encoded within the layers of a Deep Neural Network (DNN), coined GFPNet. The objective of the network is to transfer the particularities of the object from the scene to the raw volume represented by the GP. We show that GFPNet competes with state of the art shape completion methods by providing performance results on the ModelNet and KITTI benchmarking datasets.
