Lee Yangming
RoCAL Lab, Rochester Institute of Technology, Rochester, NY 14623, USA.
Sensors (Basel). 2024 Sep 10;24(18):5861. doi: 10.3390/s24185861.
Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in dynamic or complex environments. This work provides a comprehensive overview of classical 3D dense reconstruction techniques, including those based on geometric and optical models, as well as approaches leveraging deep learning. It also discusses the datasets used for deep learning and evaluates the performance and the strengths and limitations of deep learning methods on these datasets.
三维密集重建涉及从二维图像中提取三维物体的完整形状和纹理细节。尽管三维重建是一个关键且经过充分研究的领域，但在动态或复杂环境中，它仍然是一个尚未解决的挑战。这项工作全面概述了经典的三维密集重建技术，包括基于几何和光学模型的技术，以及利用深度学习的方法。它还讨论了用于深度学习的数据集，并评估了深度学习方法在这些数据集上的性能以及优缺点。
