Point-based rendering enhancement via deep learning
Point-based rendering enhancement via deep learning
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DOI：
10.1007/s00371-018-1550-6
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Giang Bui;Truc Le;Brittany Morago;Y. Duan
中科院分区：
文献类型：
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作者：
Giang Bui;Truc Le;Brittany Morago;Y. Duan
当前最先进的点渲染技术，如水滴渲染，通常需要非常高分辨率的点云来创建高质量的照片级真实感渲染。获取这些设备可能非常耗时，而且通常还需要高端昂贵的扫描仪。本文提出了一种新的基于深度学习的方法，可以从低分辨率的点云生成高分辨率的照片真实感的点绘制。更具体地说，我们建议使用联合配准的高质量照片作为地面真实数据来训练用于基于点的绘制的深度神经网络。该方法能够高效地生成高质量的点绘制图像，可用于大规模3D场景的交互导航和基于图像的定位。在合成数据集和真实数据集上的大量量化评估表明，该方法的性能优于最先进的方法。
Current state-of-the-art point rendering techniques such as splat rendering generally require very high-resolution point clouds in order to create high-quality photo realistic renderings. These can be very time consuming to acquire and oftentimes also require high-end expensive scanners. This paper proposes a novel deep learning-based approach that can generate high-resolution photo realistic point renderings from low-resolution point clouds. More specifically, we propose to use co-registered high-quality photographs as the ground truth data to train the deep neural network for point-based rendering. The proposed method can generate high-quality point rendering images very efficiently and can be used for interactive navigation of large-scale 3D scenes as well as image-based localization. Extensive quantitative evaluations on both synthetic and real datasets show that the proposed method outperforms state-of-the-art methods.