Efficient Multi-View 3D Dense Matching for Large-Scale Aerial Images Using a Divide-and-Conquer Scheme

Xue, Junshi; Chen, Xiangning; Yi, Hui

doi:10.1109/cac.2018.8623429

Cited by 1 publication

(1 citation statement)

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“…However, these methods usually require complex computation for high-quality depth map estimation. To expand the reconstruction scale to a larger extent at a lower computational cost, Xue et al [31] proposed a novel multi-view 3D dense matching method for large-scale aerial images using a divide-and-conquer scheme, and Mostegel et al [32] innovatively proposed to prioritize the depth map computation of MVS by confidence prediction to efficiently obtain compact 3D point clouds with high quality and completeness. Wei et al [33] proposed a novel selective joint bilateral upsampling and depth propagation strategy for high-resolution unstructured MVS.…”

Section: Depth-map Merging Based Methodsmentioning

confidence: 99%

DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes

et al. 2021

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This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.

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Section: Depth-map Merging Based Methodsmentioning

confidence: 99%