{"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/hierarchical-surface-prediction-for-3d-object","title":"Hierarchical Surface Prediction for 3D Object Reconstruction","arxiv_id":"1704.00710","date":"2017-04-03","proceeding":null,"authors":["Christian Häne","Shubham Tulsiani","Jitendra Malik"],"abstract":"Recently, Convolutional Neural Networks have shown promising results for 3D\ngeometry prediction. They can make predictions from very little input data such\nas a single color image. A major limitation of such approaches is that they\nonly predict a coarse resolution voxel grid, which does not capture the surface\nof the objects well. We propose a general framework, called hierarchical\nsurface prediction (HSP), which facilitates prediction of high resolution voxel\ngrids. The main insight is that it is sufficient to predict high resolution\nvoxels around the predicted surfaces. The exterior and interior of the objects\ncan be represented with coarse resolution voxels. Our approach is not dependent\non a specific input type. We show results for geometry prediction from color\nimages, depth images and shape completion from partial voxel grids. Our\nanalysis shows that our high resolution predictions are more accurate than low\nresolution predictions.","url_abs":"http://arxiv.org/abs/1704.00710v2","url_pdf":"http://arxiv.org/pdf/1704.00710v2.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":"hierarchical-surface-prediction-for-3d-object","repo_url":"https://github.com/vnoves/aectech2019-sketchto3d-backend","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-geometry-prediction","task_name":"3D Geometry Prediction"},{"task_slug":"3d-object-reconstruction","task_name":"3D Object Reconstruction"},{"task_slug":"3d-geometry","task_name":"3D geometry"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-reconstruction","task_name":"Object Reconstruction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1704.00710","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}