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3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset

arXiv 2304.09371 ↗First posted 19 Apr 2023cs.CV cs.ROcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 57 of its 146 references that are in the map. First posted on arXiv 19 Apr 2023.

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57 of 146 references.

References · 57

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38 more
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