论文标题
使用深神经网络从拼字图中重建车辆
Reconstructing vehicles from orthographic drawings using deep neural networks
论文作者
论文摘要
本文探讨了使用深神经网络从多个拼字图图中重建对象重建的当前最新。它提出了两种算法来从单个图像中提取多个视图。本文提出了一个基于像素对齐的隐式函数（PIFU）的系统，并制定了一个高级采样策略来生成签名的距离样本。它还将这种方法与从多个视图的深度图回归进行了比较。此外，本文使用了一个新颖的数据集来进行赛车游戏Assetto Corsa的车辆重建，该数据集的质量比常用的Shapenet数据集更高。受过训练的神经网络很好地概括了现实世界的输入，并创建了合理且详细的重建。
This paper explores the current state-of-the-art of object reconstruction from multiple orthographic drawings using deep neural networks. It proposes two algorithms to extract multiple views from a single image. The paper proposes a system based on pixel-aligned implicit functions (PIFu) and develops an advanced sampling strategy to generate signed distance samples. It also compares this approach to depth map regression from multiple views. Additionally, the paper uses a novel dataset for vehicle reconstruction from the racing game Assetto Corsa, which features higher quality models than the commonly used ShapeNET dataset. The trained neural network generalizes well to real-world inputs and creates plausible and detailed reconstructions.
