3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset
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.
PDF AbstractTasks
KITTI
ShapeNet
NYUv2
Matterport3D
Middlebury
TUM RGB-D
SUN3D
BlendedMVS
ApolloScape
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