3D scene reconstruction using a texture probabilistic grammar
3D scene reconstruction using a texture probabilistic grammar
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DOI：
10.1007/s11042-018-6052-z
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发表时间：
2018-05
影响因子：
3.6
通讯作者：
Dan Li;Disheng Hu;Yuke Sun;Yingsong Hu
中科院分区：
文献类型：
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作者：
Dan Li;Disheng Hu;Yuke Sun;Yingsong Hu
本文首次定义了纹理概率文法。我们已经开发了一种算法，通过训练的纹理概率语法从预先建立的模型库中获得的三维信息的2D场景。训练好的纹理概率文法也可以应用于三维重建。我们的详细流程包括：该方法首先将二维场景分割成纹理片段，然后为纹理片段分配最合适的三维对象标签，最后利用纹理概率文法预测二维场景图像中纹理片段的三维信息，最后构造原始二维场景图像的三维模型。通过实验证明，该算法对室内场景和建筑物结构的重建具有较好的效果，优于传统的基于点云的重建方法上级。通过对不同数据集和重建对象的测试，验证了算法的鲁棒性。因此，我们的算法是能够处理大量的场景具有相似的语义，它也是足够快，以处理在线三维重建。
In this paper, texture probabilistic grammar is defined for the first time. We have developed an algorithm to obtain the 3D information in a 2D scene by training the texture probabilistic grammar from the prebuilt model library. The well-trained texture probabilistic grammar could also be applied to 3D reconstruction. Our detailed process contains: dividing the 2D scene into texture fragments; assigning the most suitable 3D object label to the 2D texture fragments; using our texture probabilistic grammar to predict 3D information of the texture fragments in 2D scene image; constructing the 3D model of the original 2D scene image. Through experiments, it is proved that the algorithm has a better effect on reconstruction of indoor scenes and building structures, and the algorithm is superior to the traditional reconstruction method based on point clouds. Different datasets and reconstructed objects are tested, which verifies the robustness of the algorithm. As a result, our algorithm is able to deal with the large numbers of scenes with similar semantics and it is also fast enough to deal with the online 3D reconstruction.