3D Reconstruction Based on the Depth Image: A Review

Mi, Qingwei

doi:10.1007/978-3-031-08819-3_17

Cited by 3 publications

(1 citation statement)

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“…Three-dimensional reconstruction refers to the establishment of a mathematical model suitable for computer representation and processing of three-dimensional objects. It has been widely used in the fields of virtual reality, robotics, automatic driving, and land resource utilization [ 1 , 2 ]. Multi View Stereo (MVS) [ 3 ] restores dense representations of three-dimensional scenes from multi-view images and calibrated cameras.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised 3D Reconstruction with Multi-Measure and High-Resolution Loss

Zheng

Luo

Chen

et al. 2022

Sensors

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Multi-view 3D reconstruction technology based on deep learning is developing rapidly. Unsupervised learning has become a research hotspot because it does not need ground truth labels. The current unsupervised method mainly uses 3DCNN to regularize the cost volume to regression image depth. This approach results in high memory requirements and long computing time. In this paper, we propose an end-to-end unsupervised multi-view 3D reconstruction network framework based on PatchMatch, Unsup_patchmatchnet. It dramatically reduces memory requirements and computing time. We propose a feature point consistency loss function. We incorporate various self-supervised signals such as photometric consistency loss and semantic consistency loss into the loss function. At the same time, we propose a high-resolution loss method. This improves the reconstruction of high-resolution images. The experiment proves that the memory usage of the network is reduced by 80% and the running time is reduced by more than 50% compared with the network using 3DCNN method. The overall error of reconstructed 3D point cloud is only 0.501 mm. It is superior to most current unsupervised multi-view 3D reconstruction networks. Then, we test on different data sets and verify that the network has good generalization.

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Section: Introductionmentioning

confidence: 99%