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• W3212304386 abstract "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." @default.
• W3212304386 created "2021-11-22" @default.
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• W3212304386 date "2021-11-13" @default.
• W3212304386 modified "2023-09-29" @default.
• W3212304386 title "DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes" @default.
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• W3212304386 doi "https://doi.org/10.3390/rs13224569" @default.
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