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README.md
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## Motivation π‘
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To rigorously assess the generalization capability of **CoherentGS** in complex, unconstrained outdoor environments, we establish a new benchmark named
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> **Citation Reference:** Ling et al. (2024). DL3DV-10K: A Large-scale Dataset for Deep Learning-based 3D Vision.
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## Dataset Source π
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β β βββ 3views/ # 3-View Sub-set
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β β β βββ images/ # Raw input image files
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β β β βββ ref_image/ # Reference Image
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β β β βββ sparse/ # Sparse reconstruction results
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β β β βββ cameras.json # Camera parameter file
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β β β βββ ext_metadata.json # Additional metadata
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β β β βββ hold=7 # Test set configuration
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β β β βββ intrinsics.json # Camera intrinsics
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β β β βββ poses_bounds.npy # Camera poses and scene bounds
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β β β βββ train_test_split_3.json # Train/Test split definition
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β β β βββ transforms.json # Coordinate transformation info
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β β βββ 6views/ # 6-View Sub-set
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β β βββ 9views/ # 9-View Sub-set
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β βββ 0002/
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## Motivation π‘
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To rigorously assess the generalization capability of **CoherentGS** in complex, unconstrained outdoor environments, we establish a new benchmark named **DL3DV-Blur**. This benchmark is derived from five diverse scenes within the DL3DV-10K dataset.
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> **Citation Reference:** Ling et al. (2024). DL3DV-10K: A Large-scale Dataset for Deep Learning-based 3D Vision.
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> [https://arxiv.org/abs/2312.16256](https://arxiv.org/abs/2312.16256)
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## Dataset Source π
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β β βββ 3views/ # 3-View Sub-set
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β β β βββ images/ # Raw input image files
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β β β βββ ref_image/ # Reference Image
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β β β βββ sparse/ # Sparse reconstruction results (e.g., COLMAP output)
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β β β βββ cameras.json # Camera parameter file
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β β β βββ ext_metadata.json # Additional metadata
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β β β βββ hold=7 # Test set configuration
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β β β βββ intrinsics.json # Camera intrinsics
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β β β βββ poses_bounds.npy # Camera poses and scene bounds
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β β β βββ train_test_split_3.json # Train/Test split definition
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β β β βββ transforms.json # Coordinate transformation info
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β β βββ 6views/ # 6-View Sub-set
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β β βββ 9views/ # 9-View Sub-set
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β βββ 0002/
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