{"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/featurenerf-learning-generalizable-nerfs-by","title":"FeatureNeRF: Learning Generalizable NeRFs by Distilling Foundation Models","arxiv_id":"2303.12786","date":"2023-03-22","proceeding":"ICCV 2023 1","authors":["Jianglong Ye","Naiyan Wang","Xiaolong Wang"],"abstract":"Recent works on generalizable NeRFs have shown promising results on novel view synthesis from single or few images. However, such models have rarely been applied on other downstream tasks beyond synthesis such as semantic understanding and parsing. In this paper, we propose a novel framework named FeatureNeRF to learn generalizable NeRFs by distilling pre-trained vision foundation models (e.g., DINO, Latent Diffusion). FeatureNeRF leverages 2D pre-trained foundation models to 3D space via neural rendering, and then extract deep features for 3D query points from NeRF MLPs. Consequently, it allows to map 2D images to continuous 3D semantic feature volumes, which can be used for various downstream tasks. We evaluate FeatureNeRF on tasks of 2D/3D semantic keypoint transfer and 2D/3D object part segmentation. Our extensive experiments demonstrate the effectiveness of FeatureNeRF as a generalizable 3D semantic feature extractor. Our project page is available at https://jianglongye.com/featurenerf/ .","url_abs":"https://arxiv.org/abs/2303.12786v1","url_pdf":"https://arxiv.org/pdf/2303.12786v1.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":"featurenerf-learning-generalizable-nerfs-by","repo_url":"https://github.com/jianglongye/featurenerf","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"},{"task_slug":"neural-rendering","task_name":"Neural Rendering"},{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"vision-transformer","method_name":"Vision Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2303.12786","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.12786"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/jianglongye/featurenerf","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":1,"unverified":6},"by_repo_kind":{"official":{"samples":7,"ran":1,"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":"bf28ce0de1a71cef","entry":"get_alpha_loss","repo":"jianglongye/featurenerf","repo_kind":"official","path":"src/model/loss.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/src/model/loss.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":true,"mcp_get_code":{"code_sha256":"bf28ce0de1a71cef"}},{"code_sha256_prefix":"2efc16adaed41431","entry":"extra_args","repo":"jianglongye/featurenerf","repo_kind":"official","path":"eval/eval_2d_task.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/eval/eval_2d_task.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":true,"mcp_get_code":{"code_sha256":"2efc16adaed41431"}},{"code_sha256_prefix":"2c5e306529ece6cf","entry":"get_rgb_loss","repo":"jianglongye/featurenerf","repo_kind":"official","path":"src/model/loss.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/src/model/loss.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":true,"mcp_get_code":{"code_sha256":"2c5e306529ece6cf"}},{"code_sha256_prefix":"09578629f7267e4b","entry":"make_encoder","repo":"jianglongye/featurenerf","repo_kind":"official","path":"src/model/model_util.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/src/model/model_util.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":true,"mcp_get_code":{"code_sha256":"09578629f7267e4b"}},{"code_sha256_prefix":"ea00c4d267522342","entry":"make_mlp","repo":"jianglongye/featurenerf","repo_kind":"official","path":"src/model/model_util.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/src/model/model_util.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":true,"mcp_get_code":{"code_sha256":"ea00c4d267522342"}},{"code_sha256_prefix":"2cb93c8f9b1fad49","entry":"seg_map_to_vis_map","repo":"jianglongye/featurenerf","repo_kind":"official","path":"eval/eval_2d_task.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/eval/eval_2d_task.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":true,"mcp_get_code":{"code_sha256":"2cb93c8f9b1fad49"}},{"code_sha256_prefix":"fee53d3b28454688","entry":"seg_map_to_vis_map","repo":"jianglongye/featurenerf","repo_kind":"official","path":"eval/eval_3d_task.py","file_url":"https://github.com/jianglongye/featurenerf/blob/HEAD/eval/eval_3d_task.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":true,"mcp_get_code":{"code_sha256":"fee53d3b28454688"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}