{"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/nope-nerf-optimising-neural-radiance-field","title":"NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior","arxiv_id":"2212.07388","date":"2022-12-14","proceeding":"CVPR 2023 1","authors":["Wenjing Bian","ZiRui Wang","Kejie Li","Jia-Wang Bian","Victor Adrian Prisacariu"],"abstract":"Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this challenging problem by incorporating undistorted monocular depth priors. These priors are generated by correcting scale and shift parameters during training, with which we are then able to constrain the relative poses between consecutive frames. This constraint is achieved using our proposed novel loss functions. Experiments on real-world indoor and outdoor scenes show that our method can handle challenging camera trajectories and outperforms existing methods in terms of novel view rendering quality and pose estimation accuracy. Our project page is https://nope-nerf.active.vision.","url_abs":"https://arxiv.org/abs/2212.07388v3","url_pdf":"https://arxiv.org/pdf/2212.07388v3.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":"nope-nerf-optimising-neural-radiance-field","repo_url":"https://github.com/ActiveVisionLab/nope-nerf","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":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.07388","atlas_url":"https://app.syntology.ai/?focus=2212.07388","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.07388"}},"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/ActiveVisionLab/nope-nerf","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":5},"by_repo_kind":{"official":{"samples":5,"ran":5,"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":"1cfdd8b5660fd081","entry":"arange_pixels","repo":"ActiveVisionLab/nope-nerf","repo_kind":"official","path":"model/common.py","file_url":"https://github.com/ActiveVisionLab/nope-nerf/blob/HEAD/model/common.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":"1cfdd8b5660fd081"}},{"code_sha256_prefix":"74072d2f0e501339","entry":"encode_position","repo":"ActiveVisionLab/nope-nerf","repo_kind":"official","path":"model/official_nerf.py","file_url":"https://github.com/ActiveVisionLab/nope-nerf/blob/HEAD/model/official_nerf.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":true,"mcp_get_code":{"code_sha256":"74072d2f0e501339"}},{"code_sha256_prefix":"0a96d4d9cdb21375","entry":"get_mask","repo":"ActiveVisionLab/nope-nerf","repo_kind":"official","path":"model/common.py","file_url":"https://github.com/ActiveVisionLab/nope-nerf/blob/HEAD/model/common.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":"0a96d4d9cdb21375"}},{"code_sha256_prefix":"71dbed7bd8b18214","entry":"is_url","repo":"ActiveVisionLab/nope-nerf","repo_kind":"official","path":"model/checkpoints.py","file_url":"https://github.com/ActiveVisionLab/nope-nerf/blob/HEAD/model/checkpoints.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":"71dbed7bd8b18214"}},{"code_sha256_prefix":"cc20eeee3ba3cb0d","entry":"to_pytorch","repo":"ActiveVisionLab/nope-nerf","repo_kind":"official","path":"model/common.py","file_url":"https://github.com/ActiveVisionLab/nope-nerf/blob/HEAD/model/common.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":true,"mcp_get_code":{"code_sha256":"cc20eeee3ba3cb0d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}