Open Access
Review
Table 3
Advantages and disadvantages of common visual 3D reconstruction algorithms.
Common visual 3D reconstruction algorithms | References | Advantages | Disadvantages |
---|---|---|---|
Incremental SFM | Schönberger and Frahm [82], Yin and Yu [84] | The performance is robust and the reconstruction precision is high | Affected by the initial image on the selection and camera add order, the cumulative error is large and the efficiency is not high in the reconstruction of large scenes |
Global SFM | Moulon et al. [80] | Not affected by the initial image pair and the order of camera addition, the cumulative error is small, and the reconstruction efficiency is high | The robustness is not good, and the completeness of scene reconstruction is insufficient |
Hybrid SFM | Cui et al. [83] | The cumulative error is small and the robustness is good | The efficiency is not high |
Voxel based MVS | Sinha et al. [87] | The generated point cloud is regular and mesh is easy to extract | Reconstruction accuracy is related to voxel particle size, and it is difficult to deal with large scenes |
Feature point growing based MVS | Lin et al. [88] | The point cloud has high precision and uniform distribution | Areas with weak textures are prone to holes and require reading all images at once |
Depth-map merging based MVS | Seitz et al. [86], Lindenberger et al. [89], Zhou et al. [90] | It can be used in parallel computation for 3D reconstruction of large scenes, and the number of point clouds obtained is large | Too dependent on neighborhood image group selection |
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