Reorganize dataset: per-scene zips for COLMAP and NCore V4 formats
Browse files- README.md +6 -2
- ppisp_dataset.zip → colmap/huerstholz_colmap.zip +2 -2
- colmap/struktur28_colmap.zip +3 -0
- colmap/toro_colmap.zip +3 -0
- colmap/valiant_colmap.zip +3 -0
- ncore/huerstholz_ncore.zip +3 -0
- ncore/struktur28_ncore.zip +3 -0
- ncore/toro_ncore.zip +3 -0
- ncore/valiant_ncore.zip +3 -0
README.md
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- novel-view-synthesis
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- radiance-fields
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- photometric-calibration
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pretty_name: PPISP Dataset
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size_categories:
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- 1K<n<10K
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Each scene has two variants: a standard version processed from the full exposure brackets, and an auto version where selected exposures were re-processed with automatic exposure compensation and white balancing, for a total of eight sequences. We also provide pre-computed COLMAP sparse reconstructions (camera poses and point clouds) for each sequence, enabling direct usage in common radiance field reconstruction methods.
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This dataset is ready for commercial/non-commercial use.
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## Dataset Owner(s):
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We used COLMAP to reconstruct camera poses and a sparse point cloud for each sequence.
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## Dataset Format
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Downsampled images in JPEG format, COLMAP camera poses, COLMAP sparse point cloud.
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## Dataset Quantification
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8 sequences, about 2600 photos in JPEG format total. The four standard sequences contain 500-700 images each; the four auto sequences contain 50-150 images each.
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Measurement of
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## Reference(s):
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[Project Page](https://research.nvidia.com/labs/sil/projects/ppisp/)
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- novel-view-synthesis
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- radiance-fields
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- photometric-calibration
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- ncore
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pretty_name: PPISP Dataset
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size_categories:
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- 1K<n<10K
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Each scene has two variants: a standard version processed from the full exposure brackets, and an auto version where selected exposures were re-processed with automatic exposure compensation and white balancing, for a total of eight sequences. We also provide pre-computed COLMAP sparse reconstructions (camera poses and point clouds) for each sequence, enabling direct usage in common radiance field reconstruction methods.
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The dataset is provided in two formats: COLMAP (`colmap/`) and NCore V4 (`ncore/`). Each format contains one zip archive per scene, with both the standard and auto variants as separate folders (e.g., `huerstholz/` and `huerstholz_auto/`).
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This dataset is ready for commercial/non-commercial use.
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## Dataset Owner(s):
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We used COLMAP to reconstruct camera poses and a sparse point cloud for each sequence.
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## Dataset Format
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- **COLMAP format** (`colmap/`): Downsampled images in JPEG format, COLMAP camera poses, COLMAP sparse point cloud.
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- **NCore V4 format** (`ncore/`): NCore V4 scene archives and metadata.
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## Dataset Quantification
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8 sequences, about 2600 photos in JPEG format total. The four standard sequences contain 500-700 images each; the four auto sequences contain 50-150 images each.
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Measurement of total data storage: About 14.3 GB in compressed form (8.1 GB COLMAP format + 6.2 GB NCore V4 format).
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## Reference(s):
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[Project Page](https://research.nvidia.com/labs/sil/projects/ppisp/)
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ppisp_dataset.zip → colmap/huerstholz_colmap.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:1d4ad8e1388f0051ee6c1266c9b9a0669c99b8b76bcc47809bb49772b404460c
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size 3401606566
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colmap/struktur28_colmap.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:cf7ab7f100da66b2bf05b178ebcfa3a950e1bf2b1d7ff64a6c7a1e1f682afa8d
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size 1535277338
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colmap/toro_colmap.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:9bb2203246d85604ef580a6664cd16bc3bdb42dc55ee4647bee28bd40372b2c5
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size 1706361859
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colmap/valiant_colmap.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:29e7292f9224907ef389facb0a616318beb04d4812c4fe6cee43258fb94385e9
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size 1958272527
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ncore/huerstholz_ncore.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd4c2f249fb84b26b8898bdf9184755a03fdd0b13ebe4840163cad62049218d7
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size 2771472158
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ncore/struktur28_ncore.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:89e548419134697b97304104f393b94fe5ce7e533fa08bc38035418b2a742b63
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size 1094683910
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ncore/toro_ncore.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:d52e6d8f5889208355dcd3d729f730adcc07008053cae30bd0f752c929d89f41
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size 1259902629
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ncore/valiant_ncore.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:804ae61204a5372a839c62512dd29557c3c6e5081f5e6094d5799ae67e77ccee
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size 1453755974
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