Datasets › LLFF

LLFF (Local Light Field Fusion)

Introduced by Ben Mildenhall et al. in Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines2 May 2019 archive 2025-07-28

Local Light Field Fusion (LLFF) is a practical and robust deep learning solution for capturing and rendering novel views of complex real-world scenes for virtual exploration. The dataset consists of both renderings and real images of natural scenes. The synthetic images are rendered from the SUNCG and UnrealCV where SUNCG contains 45000 simplistic house and room environments with texture-mapped surfaces and low geometric complexity. UnrealCV contains a few large-scale environments modeled and rendered with extreme detail. The real images are 24 scenes captured from a handheld cellphone.

Benchmarks archive 2025-07-28

All 4 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

5 shown of 5 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 340. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

bmild/nerfpytorch

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

No modality tagged.

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • LLFF

1 variant name, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections