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- W4360930751 abstract "Multi-view stereo (MVS) aims to measure the precise surface depth of a scene from observations at multiple photography angles and then densely reconstruct its 3D geometry information. Learning-based MVS approaches have been dominantly popular for their robustness to low texture areas and non-Lambertian surfaces. However, most existing methods focus on estimating depth maps for input images by constructing global cost volumes and designing ingenious yet large variance-based 3D-CNN networks for cost volume regularization. Such approaches ignore the co-visible relation embedded in multiple views, resulting in heavy computation, erroneous cost aggregation from invisible views, and finally inaccurate 3D reconstruction results. In this paper, we propose a Co-visibility Reasoning Multi-View Stereo Network (CR-MVSNet) to explore the co-visible relations hidden in multiple views for reliable multi-view similarity measurement and efficient reconstruction. Precisely, the proposed Co-visibility Reasoning Cost Aggregation (CRCA) module includes the adaptive inter-cost volume aggregation via mining the uncertainty of co-visibility relations in multiple views and the adaptive intra-cost volume aggregation by exploiting spatial contextual information. Moreover, the cost volumes are constructed via the proposed global-to-patch manner to speed up computation. Experimental results show that our approach achieves the best overall performance on the DTU, Tanks and Temples, and ETH3D-test datasets over recent state-of-the-art MVS algorithms. The consistently favorable results on three datasets with completely different depth ranges proved the superiority and generalizability of CR-MVSNet." @default.
- W4360930751 created "2023-03-26" @default.
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- W4360930751 date "2023-01-01" @default.
- W4360930751 modified "2023-10-12" @default.
- W4360930751 title "Incorporating Co-Visibility Reasoning Into Surface Depth Measurement" @default.
- W4360930751 doi "https://doi.org/10.1109/tim.2023.3250231" @default.
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