{"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/garf-gaussian-activated-radiance-fields-for","title":"GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation","arxiv_id":"2204.05735","date":"2022-04-12","proceeding":null,"authors":["Shin-Fang Chng","Sameera Ramasinghe","Jamie Sherrah","Simon Lucey"],"abstract":"Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses. Although approaches for jointly recovering the radiance field and camera pose exist (BARF), they rely on a cumbersome coarse-to-fine auxiliary positional embedding to ensure good performance. We present Gaussian Activated neural Radiance Fields (GARF), a new positional embedding-free neural radiance field architecture - employing Gaussian activations - that outperforms the current state-of-the-art in terms of high fidelity reconstruction and pose estimation.","url_abs":"https://arxiv.org/abs/2204.05735v1","url_pdf":"https://arxiv.org/pdf/2204.05735v1.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":"garf-gaussian-activated-radiance-fields-for","repo_url":"https://github.com/sfchng/Gaussian-Activated-Radiance-Fields","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"garf-gaussian-activated-radiance-fields-for","repo_url":"https://github.com/laura-a-n-n/tf-garf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"gone","observed_at":"2026-09-17","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"novel-view-synthesis","task_name":"Novel View Synthesis"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/novel-view-synthesis-on-bleff","task":"Novel View Synthesis","dataset":"BLEFF","model":"GARF","rank_in_archive_order":3,"of":3,"metrics":{"PSNR/SSIM":"25.80/0.76"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.05735","atlas_url":"https://app.syntology.ai/?focus=2204.05735","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}