Semantic Scene Reconstruction Using the DenseCRF Model

Zhixin Ma, Chong Cao, Xukun Shen
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Abstract

With the rapid growth of virtual reality industry, fast and accurate algorithms for scene reconstruction and understanding became the research focus in related fields. Traditional methods always consider the 3D model and scene understanding as two problems and work them out separately. In this paper, we propose a new method to reconstruct semantic 3D models from multi-view images. This method not only contains information of points in 3D space, but also builds up their relationship with pixels from images. We commit experiments on four real challenging datasets to test the effectiveness of our proposed method. The reconstruction can be directly applied to virtual reality applications, such as roaming in 3D scenes.
使用DenseCRF模型的语义场景重建
随着虚拟现实产业的快速发展,快速准确的场景重建与理解算法成为相关领域的研究热点。传统的方法总是把三维模型和场景理解看作两个问题,分别进行求解。本文提出了一种从多视图图像中重建语义三维模型的新方法。该方法不仅包含了三维空间中点的信息,而且建立了点与图像像素的关系。我们在四个具有挑战性的真实数据集上进行了实验,以测试我们提出的方法的有效性。该重建可以直接应用于虚拟现实应用,例如在3D场景中漫游。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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