{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-neural-light-fields-with-ray-space","title":"Learning Neural Light Fields with Ray-Space Embedding Networks","arxiv_id":"2112.01523","date":"2021-12-02","proceeding":null,"authors":["Benjamin Attal","Jia-Bin Huang","Michael Zollhoefer","Johannes Kopf","Changil Kim"],"abstract":"Neural radiance fields (NeRFs) produce state-of-the-art view synthesis results. However, they are slow to render, requiring hundreds of network evaluations per pixel to approximate a volume rendering integral. Baking NeRFs into explicit data structures enables efficient rendering, but results in a large increase in memory footprint and, in many cases, a quality reduction. In this paper, we propose a novel neural light field representation that, in contrast, is compact and directly predicts integrated radiance along rays. Our method supports rendering with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel. At the core of our approach is a ray-space embedding network that maps the 4D ray-space manifold into an intermediate, interpolable latent space. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset. In addition, for forward-facing scenes with sparser inputs we achieve results that are competitive with NeRF-based approaches in terms of quality while providing a better speed/quality/memory trade-off with far fewer network evaluations.","url_abs":"https://arxiv.org/abs/2112.01523v3","url_pdf":"https://arxiv.org/pdf/2112.01523v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-neural-light-fields-with-ray-space","repo_url":"https://github.com/facebookresearch/neural-light-fields","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"nerf","task_name":"NeRF"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2112.01523","atlas_url":"https://app.syntology.ai/?focus=2112.01523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.01523"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/facebookresearch/neural-light-fields","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":4,"unverified":1},"by_repo_kind":{"official":{"samples":5,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":5,"samples":[{"code_sha256_prefix":"5b67ca5168302b10","entry":"fft_rgb","repo":"facebookresearch/neural-light-fields","repo_kind":"official","path":"datasets/fourier.py","file_url":"https://github.com/facebookresearch/neural-light-fields/blob/HEAD/datasets/fourier.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"5b67ca5168302b10"}},{"code_sha256_prefix":"c5ed1bf10b125abe","entry":"fuse_outputs_default","repo":"facebookresearch/neural-light-fields","repo_kind":"official","path":"nlf/models.py","file_url":"https://github.com/facebookresearch/neural-light-fields/blob/HEAD/nlf/models.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"c5ed1bf10b125abe"}},{"code_sha256_prefix":"c832923b546e9fb3","entry":"mse","repo":"facebookresearch/neural-light-fields","repo_kind":"official","path":"metrics.py","file_url":"https://github.com/facebookresearch/neural-light-fields/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"c832923b546e9fb3"}},{"code_sha256_prefix":"1818f59a8d63858c","entry":"psnr","repo":"facebookresearch/neural-light-fields","repo_kind":"official","path":"metrics.py","file_url":"https://github.com/facebookresearch/neural-light-fields/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"1818f59a8d63858c"}},{"code_sha256_prefix":"dfe18774512d687b","entry":"ssim","repo":"facebookresearch/neural-light-fields","repo_kind":"official","path":"metrics.py","file_url":"https://github.com/facebookresearch/neural-light-fields/blob/HEAD/metrics.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"mcp_get_code":{"code_sha256":"dfe18774512d687b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}