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We propose a novel framework,\nnamely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects\nfrom a probabilistic space by leveraging recent advances in volumetric\nconvolutional networks and generative adversarial nets. The benefits of our\nmodel are three-fold: first, the use of an adversarial criterion, instead of\ntraditional heuristic criteria, enables the generator to capture object\nstructure implicitly and to synthesize high-quality 3D objects; second, the\ngenerator establishes a mapping from a low-dimensional probabilistic space to\nthe space of 3D objects, so that we can sample objects without a reference\nimage or CAD models, and explore the 3D object manifold; third, the adversarial\ndiscriminator provides a powerful 3D shape descriptor which, learned without\nsupervision, has wide applications in 3D object recognition. Experiments\ndemonstrate that our method generates high-quality 3D objects, and our\nunsupervisedly learned features achieve impressive performance on 3D object\nrecognition, comparable with those of supervised learning methods.","url_abs":"http://arxiv.org/abs/1610.07584v2","url_pdf":"http://arxiv.org/pdf/1610.07584v2.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":"learning-a-probabilistic-latent-space-of","repo_url":"https://github.com/black0017/3D-GAN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"learning-a-probabilistic-latent-space-of","repo_url":"https://github.com/chinokenochkan/3dgan-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"learning-a-probabilistic-latent-space-of","repo_url":"https://github.com/zck119/3dgan-release","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"torch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-object-recognition","task_name":"3D Object Recognition"},{"task_slug":"3d-point-cloud-linear-classification","task_name":"3D Point Cloud Linear Classification"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"unsupervised-3d-point-cloud-linear-evaluation","task_name":"Unsupervised 3D Point Cloud Linear Evaluation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-point-cloud-linear-classification-on","task":"3D Point Cloud Linear Classification","dataset":"ModelNet40","model":"3D-GAN","rank_in_archive_order":20,"of":20,"metrics":{"Overall Accuracy":"83.3"},"uses_additional_data":false},{"leaderboard":"/sota/3d-shape-retrieval-on-pix3d","task":"3D Shape Classification","dataset":"Pix3D","model":"3D-VAE-GAN","rank_in_archive_order":3,"of":3,"metrics":{"R@1":"0.02","R@16":"0.21","R@2":"0.03","R@32":"0.34","R@4":"0.07","R@8":"0.12"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1610.07584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1610.07584"}},"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. 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