{"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/bringing-nerfs-to-the-latent-space-inverse","title":"Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder","arxiv_id":"2410.22936","date":"2024-10-30","proceeding":null,"authors":["Antoine Schnepf","Karim Kassab","Jean-Yves Franceschi","Laurent Caraffa","Flavian vasile","Jeremie Mary","Andrew Comport","Valerie Gouet-Brunet"],"abstract":"While pre-trained image autoencoders are increasingly utilized in computer vision, the application of inverse graphics in 2D latent spaces has been under-explored. Yet, besides reducing the training and rendering complexity, applying inverse graphics in the latent space enables a valuable interoperability with other latent-based 2D methods. The major challenge is that inverse graphics cannot be directly applied to such image latent spaces because they lack an underlying 3D geometry. In this paper, we propose an Inverse Graphics Autoencoder (IG-AE) that specifically addresses this issue. To this end, we regularize an image autoencoder with 3D-geometry by aligning its latent space with jointly trained latent 3D scenes. We utilize the trained IG-AE to bring NeRFs to the latent space with a latent NeRF training pipeline, which we implement in an open-source extension of the Nerfstudio framework, thereby unlocking latent scene learning for its supported methods. We experimentally confirm that Latent NeRFs trained with IG-AE present an improved quality compared to a standard autoencoder, all while exhibiting training and rendering accelerations with respect to NeRFs trained in the image space. Our project page can be found at https://ig-ae.github.io .","url_abs":"https://arxiv.org/abs/2410.22936v1","url_pdf":"https://arxiv.org/pdf/2410.22936v1.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":[],"tasks":[{"task_slug":"3d-geometry","task_name":"3D geometry"},{"task_slug":"nerf","task_name":"NeRF"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.22936","atlas_url":"https://app.syntology.ai/?focus=2410.22936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.22936"}},"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":"deterministic:regex_extraction","url":"https://github.com/k-kassab/igae","reach":null}],"summary":{"ran_fixture":1,"ran_violates":2,"unverified":1},"by_repo_kind":{"found_in_text":{"samples":4,"ran":3,"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":4,"samples":[{"code_sha256_prefix":"fbd09454c193cf8e","entry":"compute_tv","repo":"k-kassab/igae","repo_kind":"found_in_text","path":"igae_training/ae/trainers.py","file_url":"https://github.com/k-kassab/igae/blob/HEAD/igae_training/ae/trainers.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"fbd09454c193cf8e"}},{"code_sha256_prefix":"caea6c2922cce841","entry":"use_decoder_in_consistency","repo":"k-kassab/igae","repo_kind":"found_in_text","path":"igae_training/ae/trainers.py","file_url":"https://github.com/k-kassab/igae/blob/HEAD/igae_training/ae/trainers.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"caea6c2922cce841"}},{"code_sha256_prefix":"1e8e070cb79afaca","entry":"use_encoder_in_consistency","repo":"k-kassab/igae","repo_kind":"found_in_text","path":"igae_training/ae/trainers.py","file_url":"https://github.com/k-kassab/igae/blob/HEAD/igae_training/ae/trainers.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1e8e070cb79afaca"}},{"code_sha256_prefix":"087f458a0077e9fa","entry":"NerfVAE","repo":"k-kassab/igae","repo_kind":"found_in_text","path":"igae_training/ae/trainers.py","file_url":"https://github.com/k-kassab/igae/blob/HEAD/igae_training/ae/trainers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"087f458a0077e9fa"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}