论文标题
跨域形状相似性学习的硬性示例生成
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
论文作者
论文摘要
基于图像的3D形状检索（IBSR）旨在从大型3D形状数据库中找到给定2D图像的相应3D形状。常见的例程是将2D图像和3D形状映射到嵌入空间中，并定义（或学习）形状相似性度量。尽管使用某些适应技术的公制学习似乎是塑造相似性学习的自然解决方案，但对于细粒的形状检索通常不令人满意。在本文中，我们确定了绩效不佳的来源，并提出了解决此问题的实用解决方案。我们发现，负对之间的形状差异与纹理间隙纠缠在一起，从而使度量学习无效地推开负面对。为了解决这个问题，我们开发了一个以几何为重点的多视图指标学习框架，该框架由纹理综合赋予了能力。 3D形模型的纹理的合成产生了硬三重，从而抑制了2D图像中丰富纹理的不利影响，从而推动网络专注于发现几何特性。我们的方法显示了最近发布的大规模3D-Future [1]存储库以及三个广泛研究的基准测试的最新性能，包括Pix3d [2]，Stanford Cars [3]和Comp Cars [4]。代码将在以下网址公开可用：https：//github.com/3d-front-future/ibsr-texture
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D shape of a given 2D image from a large 3D shape database. The common routine is to map 2D images and 3D shapes into an embedding space and define (or learn) a shape similarity measure. While metric learning with some adaptation techniques seems to be a natural solution to shape similarity learning, the performance is often unsatisfactory for fine-grained shape retrieval. In the paper, we identify the source of the poor performance and propose a practical solution to this problem. We find that the shape difference between a negative pair is entangled with the texture gap, making metric learning ineffective in pushing away negative pairs. To tackle this issue, we develop a geometry-focused multi-view metric learning framework empowered by texture synthesis. The synthesis of textures for 3D shape models creates hard triplets, which suppress the adverse effects of rich texture in 2D images, thereby push the network to focus more on discovering geometric characteristics. Our approach shows state-of-the-art performance on a recently released large-scale 3D-FUTURE[1] repository, as well as three widely studied benchmarks, including Pix3D[2], Stanford Cars[3], and Comp Cars[4]. Codes will be made publicly available at: https://github.com/3D-FRONT-FUTURE/IBSR-texture
