IEEE Trans Image Process. 2014 Jan;23(1):308-18. doi: 10.1109/TIP.2013.2290597.
Depth-map merging based 3D modeling is an effective approach for reconstructing large-scale scenes from multiple images. In addition to generate high quality depth maps at each image, how to select suitable neighboring images for each image is also an important step in the reconstruction pipeline, unfortunately to which little attention has been paid in the literature until now. This paper is intended to tackle this issue for large scale scene reconstruction where many unordered images are captured and used with substantial varying scale and view-angle changes. We formulate the neighboring image selection as a combinatorial optimization problem and use the quantum-inspired evolutionary algorithm to seek its optimal solution. Experimental results on the ground truth data set show that our approach can significantly improve the quality of the depth-maps as well as final 3D reconstruction results with high computational efficiency.
基于深度图合并的 3D 建模是一种从多张图像重建大场景的有效方法。除了为每张图像生成高质量的深度图外，如何为每张图像选择合适的邻域图像也是重建管道中的一个重要步骤，但到目前为止，文献中对此关注甚少。本文旨在解决这个问题，用于大规模场景重建，其中有许多无序的图像被捕获并使用，具有很大的尺度和视角变化。我们将邻域图像选择表示为一个组合优化问题，并使用量子启发式进化算法来寻找其最优解。在真实数据集上的实验结果表明，我们的方法可以显著提高深度图的质量，并以高计算效率获得最终的 3D 重建结果。
