{"url":"/dataset/nerf","name":"NeRF","full_name":"Neural Radiance Fields","description_markdown":"Neural Radiance Fields (NeRF) is a method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. The dataset contains three parts with the first 2 being synthetic renderings of objects called Diffuse Synthetic 360◦ and Realistic Synthetic 360◦ while the third is real images of complex scenes. Diffuse Synthetic 360◦ consists of four Lambertian objects with simple geometry. Each object is rendered at 512x512 pixels from viewpoints sampled on the upper hemisphere. Realistic Synthetic 360◦ consists of eight objects of complicated geometry and realistic non-Lambertian materials. Six of them are rendered from viewpoints sampled on the upper hemisphere and the two left are from viewpoints sampled on a full sphere with all of them at 800x800 pixels. The real images of complex scenes consist of 8 forward-facing scenes captured with a cellphone at a size of 1008x756 pixels.","description_withheld":null,"homepage":"https://www.matthewtancik.com/nerf","introduced_date":"2020-03-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/nerf-representing-scenes-as-neural-radiance","title":"NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis","first_author":"Ben Mildenhall","url":null},"license":null,"modalities":[],"tasks":[{"name":"Novel View Synthesis","url":"/task/novel-view-synthesis","datasets_with_task":"/datasets/task/novel-view-synthesis"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["NeRF"],"data_loaders":[],"num_papers_in_archive":3892,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/novel-view-synthesis-on-nerf","task":"Novel View Synthesis","dataset_variant":"NeRF","rows":12,"metrics":["PSNR","SSIM","LPIPS","Average PSNR","Size (MB)"],"first_row_in_archive_order":{"model":"Deformable Beta Splatting","paper":"/paper/deformable-beta-splatting","metrics":{"LPIPS":"0.028","PSNR":"34.66","SSIM":"0.973 "},"code_links":[{"title":"RongLiu-Leo/beta-splatting","url":"https://github.com/RongLiu-Leo/beta-splatting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deformable-beta-splatting","title":"Deformable Beta Splatting","date":"2025-01-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":5,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/compact-3d-scene-representation-via-self","title":"Compact 3D Scene Representation via Self-Organizing Gaussian Grids","date":"2023-12-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/k-planes-explicit-radiance-fields-in-space","title":"K-Planes: Explicit Radiance Fields in Space, Time, and Appearance","date":"2023-01-24","rows_on_this_dataset":5,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/one-is-all-bridging-the-gap-between-neural","title":"One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation","date":"2022-11-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mobilenerf-exploiting-the-polygon","title":"MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures","date":"2022-07-30","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":22,"samples_ran":7,"samples_unverified":15,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}