{"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/envidr-implicit-differentiable-renderer-with","title":"ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting","arxiv_id":"2303.13022","date":"2023-03-23","proceeding":"ICCV 2023 1","authors":["Ruofan Liang","Huiting Chen","Chunlin Li","Fan Chen","Selvakumar Panneer","Nandita Vijaykumar"],"abstract":"Recent advances in neural rendering have shown great potential for reconstructing scenes from multiview images. However, accurately representing objects with glossy surfaces remains a challenge for existing methods. In this work, we introduce ENVIDR, a rendering and modeling framework for high-quality rendering and reconstruction of surfaces with challenging specular reflections. To achieve this, we first propose a novel neural renderer with decomposed rendering components to learn the interaction between surface and environment lighting. This renderer is trained using existing physically based renderers and is decoupled from actual scene representations. We then propose an SDF-based neural surface model that leverages this learned neural renderer to represent general scenes. Our model additionally synthesizes indirect illuminations caused by inter-reflections from shiny surfaces by marching surface-reflected rays. We demonstrate that our method outperforms state-of-art methods on challenging shiny scenes, providing high-quality rendering of specular reflections while also enabling material editing and scene relighting.","url_abs":"https://arxiv.org/abs/2303.13022v1","url_pdf":"https://arxiv.org/pdf/2303.13022v1.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":"envidr-implicit-differentiable-renderer-with","repo_url":"https://github.com/nexuslrf/ENVIDR","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"neural-rendering","task_name":"Neural Rendering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.13022","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13022"}},"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/nexuslrf/ENVIDR","reach":null}],"summary":{"unverified":2},"by_repo_kind":{"official":{"samples":2,"ran":0,"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":"c2e724a22daac26f","entry":"curve_plot","repo":"nexuslrf/ENVIDR","repo_kind":"official","path":"nerf/render_func/cuda_ray.py","file_url":"https://github.com/nexuslrf/ENVIDR/blob/HEAD/nerf/render_func/cuda_ray.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":"c2e724a22daac26f"}},{"code_sha256_prefix":"0286f43f0499a444","entry":"run_cuda","repo":"nexuslrf/ENVIDR","repo_kind":"official","path":"nerf/render_func/cuda_ray.py","file_url":"https://github.com/nexuslrf/ENVIDR/blob/HEAD/nerf/render_func/cuda_ray.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":"0286f43f0499a444"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}