3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset
It provides a systematic overview for researchers in computer vision, but is incremental as it synthesizes existing knowledge without novel contributions.
This paper reviews algorithms and datasets for 3D dense reconstruction, summarizing classical geometric and optical methods as well as deep learning approaches, but does not present new results or concrete numbers.
3D dense reconstruction refers to the process of obtaining the complete shape and texture features of 3D objects from 2D planar images. 3D reconstruction is an important and extensively studied problem, but it is far from being solved. This work systematically introduces classical methods of 3D dense reconstruction based on geometric and optical models, as well as methods based on deep learning. It also introduces datasets for deep learning and the performance and advantages and disadvantages demonstrated by deep learning methods on these datasets.