{"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/rendernet-a-deep-convolutional-network-for","title":"RenderNet: A deep convolutional network for differentiable rendering from 3D shapes","arxiv_id":"1806.06575","date":"2018-06-18","proceeding":"NeurIPS 2018 12","authors":["Thu Nguyen-Phuoc","Chuan Li","Stephen Balaban","Yong-Liang Yang"],"abstract":"Traditional computer graphics rendering pipeline is designed for procedurally\ngenerating 2D quality images from 3D shapes with high performance. The\nnon-differentiability due to discrete operations such as visibility computation\nmakes it hard to explicitly correlate rendering parameters and the resulting\nimage, posing a significant challenge for inverse rendering tasks. Recent work\non differentiable rendering achieves differentiability either by designing\nsurrogate gradients for non-differentiable operations or via an approximate but\ndifferentiable renderer. These methods, however, are still limited when it\ncomes to handling occlusion, and restricted to particular rendering effects. We\npresent RenderNet, a differentiable rendering convolutional network with a\nnovel projection unit that can render 2D images from 3D shapes. Spatial\nocclusion and shading calculation are automatically encoded in the network. Our\nexperiments show that RenderNet can successfully learn to implement different\nshaders, and can be used in inverse rendering tasks to estimate shape, pose,\nlighting and texture from a single image.","url_abs":"http://arxiv.org/abs/1806.06575v3","url_pdf":"http://arxiv.org/pdf/1806.06575v3.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":"rendernet-a-deep-convolutional-network-for","repo_url":"https://github.com/thunguyenphuoc/RenderNet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"inverse-rendering","task_name":"Inverse Rendering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.06575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.06575"}},"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/thunguyenphuoc/RenderNet","reach":null}],"summary":{"ran_honours":1},"by_repo_kind":{"named_in_paper":{"samples":1,"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":1,"samples":[{"code_sha256_prefix":"338e725cceb41ff6","entry":"compute_pose_param","repo":"thunguyenphuoc/RenderNet","repo_kind":"named_in_paper","path":"RenderNet_demo.py","file_url":"https://github.com/thunguyenphuoc/RenderNet/blob/HEAD/RenderNet_demo.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"338e725cceb41ff6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}