论文标题
使用深层语义模板映射的车辆重建和纹理估算
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping
论文作者
论文摘要
我们介绍了顶点,这是一种有效的解决方案,可在现实世界街道环境中从未校准的单眼输入中恢复3D形状和内在纹理。为了完全利用车辆的先验模板,我们根据隐式语义模板映射提出了一种新颖的几何形状和纹理联合表示。与推断3D纹理分布的现有表示形式相比,我们的方法显式地约束了模板2D表面上的纹理分布,并避免了固定分辨率和拓扑的局限性。此外,通过将全球和本地功能融合在一起,我们的方法能够在可见和看不见的区域产生一致且详细的纹理。我们还贡献了一个新的合成数据集,该数据集包含830个具有稀疏密钥点标记的精美纹理的汽车模型,并使用基于物理的渲染(PBRT)系统渲染,并具有测量的HDRI Skymaps,以获得高度逼真的图像。实验证明了我们方法在测试数据集和野外图像上的出色性能。此外,提出的技术可以实现其他应用,例如3D车辆纹理转移和材料识别。
We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and texture joint representation, based on implicit semantic template mapping. Compared to existing representations which infer 3D texture distribution, our method explicitly constrains the texture distribution on the 2D surface of the template as well as avoids limitations of fixed resolution and topology. Moreover, by fusing the global and local features together, our approach is capable to generate consistent and detailed texture in both visible and invisible areas. We also contribute a new synthetic dataset containing 830 elaborate textured car models labeled with sparse key points and rendered using Physically Based Rendering (PBRT) system with measured HDRI skymaps to obtain highly realistic images. Experiments demonstrate the superior performance of our approach on both testing dataset and in-the-wild images. Furthermore, the presented technique enables additional applications such as 3D vehicle texture transfer and material identification.