【24h】

Learning Photometric Stereo via Manifold-based Mapping

机译:通过基于歧管的映射学习光度立体声

获取原文

摘要

Three-dimensional reconstruction technologies are fundamental problems in computer vision. Photometric stereo recovers the surface normals of a 3D object from varying shading cues, prevailing in its capability for generating fine surface normal. In recent years, deep learning-based photometric stereo methods are capable of improving the surface-normal estimation under general non-Lambertian surfaces, due to its powerful fitting ability on the non-Lambertian surface. These state-of-the-art methods however usually regress the surface normal directly from the high-dimensional features, without exploring the embedded structural information. This results in the underutilization of the information available in the features. Therefore, in this paper, we propose an efficient manifold-based framework for learning-based photometric stereo, which can better map combined high-dimensional feature spaces to low-dimensional manifolds. Extensive experiments show that our method, learning with the low-dimensional manifolds, achieves more accurate surface-normal estimation, outperforming other state-of-the-art methods on the challenging DiLiGenT benchmark dataset.
机译:三维重建技术是计算机视觉的基本问题。光度立体从不同遮光线索在其能力普遍用于产生精细表面法线恢复一个3D对象的表面法线。近年来,深学习型光度立体方法能够提高在一般非朗伯表面的表面法线估计,由于非朗伯表面上其强大的拟合能力。然而状态的最先进的这些方法通常回归表面直接从高维特征正常,没有探索嵌入的结构信息。这导致在功能提供的信息利用不足。因此,在本文中,我们提出了基于学习,光度立体高效的基于流形的框架,从而可以更好地映射组合的高维特征空间的低维流形。大量的实验表明,我们的方法中,与低维流形学习,实现更精确的表面法线估计,超越国家的最先进的其他的挑战勤奋基准数据集的方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号