{"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/combinerf-a-combination-of-regularization","title":"CombiNeRF: A Combination of Regularization Techniques for Few-Shot Neural Radiance Field View Synthesis","arxiv_id":"2403.14412","date":"2024-03-21","proceeding":null,"authors":["Matteo Bonotto","Luigi Sarrocco","Daniele Evangelista","Marco Imperoli","Alberto Pretto"],"abstract":"Neural Radiance Fields (NeRFs) have shown impressive results for novel view synthesis when a sufficiently large amount of views are available. When dealing with few-shot settings, i.e. with a small set of input views, the training could overfit those views, leading to artifacts and geometric and chromatic inconsistencies in the resulting rendering. Regularization is a valid solution that helps NeRF generalization. On the other hand, each of the most recent NeRF regularization techniques aim to mitigate a specific rendering problem. Starting from this observation, in this paper we propose CombiNeRF, a framework that synergically combines several regularization techniques, some of them novel, in order to unify the benefits of each. In particular, we regularize single and neighboring rays distributions and we add a smoothness term to regularize near geometries. After these geometric approaches, we propose to exploit Lipschitz regularization to both NeRF density and color networks and to use encoding masks for input features regularization. We show that CombiNeRF outperforms the state-of-the-art methods with few-shot settings in several publicly available datasets. We also present an ablation study on the LLFF and NeRF-Synthetic datasets that support the choices made. We release with this paper the open-source implementation of our framework.","url_abs":"https://arxiv.org/abs/2403.14412v1","url_pdf":"https://arxiv.org/pdf/2403.14412v1.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":"combinerf-a-combination-of-regularization","repo_url":"https://github.com/sarroccoluigi/combinerf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2403.14412","atlas_url":"https://app.syntology.ai/?focus=2403.14412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.14412"}},"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/sarroccoluigi/combinerf","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":0,"samples":[{"code_sha256_prefix":"00979fadb02bf8df","entry":"huber_loss","repo":"sarroccoluigi/combinerf","repo_kind":"official","path":"loss.py","file_url":"https://github.com/sarroccoluigi/combinerf/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"00979fadb02bf8df"}},{"code_sha256_prefix":"ddcd1a350d11a260","entry":"mape_loss","repo":"sarroccoluigi/combinerf","repo_kind":"official","path":"loss.py","file_url":"https://github.com/sarroccoluigi/combinerf/blob/HEAD/loss.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ddcd1a350d11a260"}},{"code_sha256_prefix":"8e28f039d311961c","entry":"get_encoder","repo":"sarroccoluigi/combinerf","repo_kind":"official","path":"encoding.py","file_url":"https://github.com/sarroccoluigi/combinerf/blob/HEAD/encoding.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8e28f039d311961c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}