{"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/3d-lmnet-latent-embedding-matching-for","title":"3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image","arxiv_id":"1807.07796","date":"2018-07-20","proceeding":null,"authors":["Priyanka Mandikal","K L Navaneet","Mayank Agarwal","R. Venkatesh Babu"],"abstract":"3D reconstruction from single view images is an ill-posed problem. Inferring\nthe hidden regions from self-occluded images is both challenging and ambiguous.\nWe propose a two-pronged approach to address these issues. To better\nincorporate the data prior and generate meaningful reconstructions, we propose\n3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first\ntrain a 3D point cloud auto-encoder and then learn a mapping from the 2D image\nto the corresponding learnt embedding. To tackle the issue of uncertainty in\nthe reconstruction, we predict multiple reconstructions that are consistent\nwith the input view. This is achieved by learning a probablistic latent space\nwith a novel view-specific diversity loss. Thorough quantitative and\nqualitative analysis is performed to highlight the significance of the proposed\napproach. We outperform state-of-the-art approaches on the task of single-view\n3D reconstruction on both real and synthetic datasets while generating multiple\nplausible reconstructions, demonstrating the generalizability and utility of\nour approach.","url_abs":"http://arxiv.org/abs/1807.07796v2","url_pdf":"http://arxiv.org/pdf/1807.07796v2.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":"3d-lmnet-latent-embedding-matching-for","repo_url":"https://github.com/val-iisc/3d-lmnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-point-cloud-reconstruction","task_name":"3D Point Cloud Reconstruction"},{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"point-cloud-reconstruction","task_name":"Point cloud reconstruction"},{"task_slug":"single-view-3d-reconstruction","task_name":"Single-View 3D Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.07796","atlas_url":"https://app.syntology.ai/?focus=1807.07796","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.07796"}},"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. 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