Single image 3D scene reconstruction based on ShapeNet models

Xue Chen, Yifan Ren, Yaoxu Song
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Abstract

The 3D scene reconstruction task is the basis for implementing mixed reality, but traditional single-image scene reconstruction algorithms are difficult to generate regularized models. It is believed that this situation is caused by a lack of prior knowledge, so we try to introduce the model collection ShapeNet 1 to solve this problem. Besides, our approach incorporates traditional model generation algorithms. The predicted artificial indoor objects as indicators will match models in ShapeNet. The refined models selected from ShapeNet will then replace the rough ones to produce the final 3D scene. These selected models from the model library will greatly improve the aesthetics of the reconstructed 3D scene. We test our method on the NYU-v2 2 dataset and achieve pleasing results. Our project is publicly available at https://sjtu-cv- 2021.github.io/Single-Image-3D-Reconstruction-Based-On-ShapeNet.
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基于ShapeNet模型的单图像三维场景重建
三维场景重建任务是实现混合现实的基础,但传统的单图像场景重建算法难以生成正则化模型。认为这种情况是由于缺乏先验知识造成的,因此我们尝试引入模型集ShapeNet 1来解决这一问题。此外,我们的方法结合了传统的模型生成算法。预测的室内人工物体作为指标将与ShapeNet中的模型相匹配。然后,从ShapeNet中选择的精细模型将取代粗糙的模型来产生最终的3D场景。这些从模型库中选择的模型将大大提高重建3D场景的美观性。我们在NYU-v2数据集上测试了我们的方法,并取得了令人满意的结果。我们的项目可以在https://sjtu-cv- 2021.github.io/Single-Image-3D-Reconstruction-Based-On-ShapeNet上公开获得。
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