{"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/deep-mesh-reconstruction-from-single-rgb","title":"Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks","arxiv_id":"1909.00321","date":"2019-09-01","proceeding":"ICCV 2019 10","authors":["Junyi Pan","Xiaoguang Han","Weikai Chen","Jiapeng Tang","Kui Jia"],"abstract":"Reconstructing the 3D mesh of a general object from a single image is now possible thanks to the latest advances of deep learning technologies. However, due to the nontrivial difficulty of generating a feasible mesh structure, the state-of-the-art approaches often simplify the problem by learning the displacements of a template mesh that deforms it to the target surface. Though reconstructing a 3D shape with complex topology can be achieved by deforming multiple mesh patches, it remains difficult to stitch the results to ensure a high meshing quality. In this paper, we present an end-to-end single-view mesh reconstruction framework that is able to generate high-quality meshes with complex topologies from a single genus-0 template mesh. The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification. While a deformation network predicts the per-vertex translations that reduce the gap between the reconstructed mesh and the ground truth, a novel topology modification network is employed to prune the error-prone faces, enabling the evolution of topology. By iterating over the two procedures, one can progressively modify the mesh topology while achieving higher reconstruction accuracy. Moreover, a boundary refinement network is designed to refine the boundary conditions to further improve the visual quality of the reconstructed mesh. Extensive experiments demonstrate that our approach outperforms the current state-of-the-art methods both qualitatively and quantitatively, especially for the shapes with complex topologies.","url_abs":"https://arxiv.org/abs/1909.00321v1","url_pdf":"https://arxiv.org/pdf/1909.00321v1.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":"deep-mesh-reconstruction-from-single-rgb","repo_url":"https://github.com/jnypan/TMNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-shape-reconstruction","task_name":"3D Shape Reconstruction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-shape-reconstruction-on-pix3d","task":"3D Shape Reconstruction","dataset":"Pix3D","model":"TMN","rank_in_archive_order":3,"of":5,"metrics":{"CD":"0.0903","EMD":"N/A","IoU":"N/A"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1909.00321","atlas_url":"https://app.syntology.ai/?focus=1909.00321","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1909.00321"}},"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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