[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-189116-105":53,"doc-detail-189116-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","synthesizing-3d-shapes-via-modeling-multi-view-depth-maps-and-silhouettes-with-deep-generative-networks","Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks","","This document evaluates multiple neural network variants for synthesizing 3D shapes by leveraging multi-view depth maps and silhouettes. It compares unconditional versus conditional settings across Depth and Silhouette metrics, and contrasts architectures such as AllVPNet, DropoutNet, and SingleVPNet under consistent training and test regimes. Reported results include accuracy, depth/silhouette errors across viewpoints, and IoU values, alongside comparisons with existing baselines like DeepPano, 3D ShapeNet, VoxNet, and MVCNN, highlighting performance differences between multi-view and single-view representations.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/synthesizing-3d-shapes-via-modeling-multi-view-depth-maps-and-silhouettes-with-deep-generative-networks/189116/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/synthesizing-3d-shapes-via-modeling-multi-view-depth-maps-and-silhouettes-with-deep-generative-networks/189116.png","ImageObject",442,249,{"name":88,"@type":89},"Lucas Martin","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-30","2026-09-03",true,{"@type":98,"interactionType":99,"userInteractionCount":47},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What inputs are modeled to synthesize 3D shapes in this work?","Question",{"text":108,"@type":109},"The document models multi-view depth maps and silhouettes as the core representations for 3D shape synthesis.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"How do unconditional and conditional settings affect Depth and Silhouette performance?",{"text":113,"@type":109},"It compares unconditional versus conditional scores for both Depth and Silhouette, showing measurable differences in the reported accuracy-related metrics.",{"name":115,"@type":106,"acceptedAnswer":116},"Which networks are compared and how are they categorized by view usage?",{"text":117,"@type":109},"The document compares AllVPNet, DropoutNet, and SingleVPNet, distinguishing multi-view methods from a single-view approach, and also includes baselines such as MVCNN, VoxNet, 3D ShapeNet, and DeepPano.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},189116,1788394427,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":47,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":125,"read_time":76},8796095360427,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","| DropoutNet Depth\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>|  |  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| SingleVPNet Depth | | | | | | | | |\n| AllVPNet Silhouettes | | \u003Cbr>| \u003Cbr>| \u003Cbr>| | | | |\n| DropoutNet\u003Cbr>Silhouettes | | \u003Cbr>| \u003Cbr>| \u003Cbr>| | | | |\n\n|  | AllVP |  | Dropout |  | SingleVP |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | Depth | Sil. | Depth | Sil. | Depth | Sil. |\n| Uncond. | 84.0 | 83.6 | 78.5 | 77.4 | 70.7 | 66.8 |\n| Cond. | 83.9 | 83.5 | 78.5 | 78.1 | 72.4 | 67.9 |\n\n| Chair | | | | | | | | |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Car | | | | | | | | |\n| Gun  |  | | | | | | | |\n| Laptop  |  | | | | | | | |\n| Table  |  | | | | | | | |\n\n| Models | Representation | Accuracy (%) |\n| --- | --- | --- |\n| DeepPano [20] | panorama | 78% |\n| 3D ShapeNet [28] | voxel | 77% |\n| VoxNet [15] | voxel | 83% |\n| MVCNN [23] | multi-view | 90% |\n| AllVPNet | multi-view | 82. 1% 􀀆 0.1 |\n| DropoutNet | multi-view | 74.2% 􀀆 0.2 |\n| SingleVPNet | single-view | 65.3% 􀀆 0.3 |\n\n\n| Network | Training Set |  | Test Set |  | Acc. (%) |\n| --- | --- | --- | --- | --- | --- |\n|  | Depth | Sil. | Depth | Sil. |  |\n| AllVP | 0.014 | 0.016 | 0.016 | 0.019 | 89.1 􀀆 0.1 |\n| Dropout | 0.022 | 0.026 | 0.023 | 0.028 | 85.5 􀀆 0.1 |\n| SingleVP | 0.028 | 0.036 | 0.029 | 0.036 | 82.7 􀀆 0.1 |\n| AllVP | 0.015 | 0.016 | 0.017 | 0.019 | 87.6 􀀆 0.1 |\n| Dropout | 0.022 | 0.026 | 0.023 | 0.028 | 84.9 􀀆 0.1 |\n| SingleVP | 0.032 | 0.039 | 0.033 | 0.030 | 80.0 􀀆 0.2 |\n\n\n| Viewpoint | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |\n| --- | --- |\n| Depth Err. | .031 .035 .036 .036 .038 .040 .040 .028 .029 .038 .038 .028 .029 .029 .029 .030 .029 .030 .030 .030 |\n| Sil. Err | .038 .043 .046 .046 .047 .050 .049 .035 .035 .047 .047 .035 .035 .036 .037 .037 .036 .037 .037 .037 |\n| Accuracy | 82 82 81 80 79 79 79 84 83 78 78 85 83 85 84 84 84 84 84 83 |\n| IoU | 74.2 70.4 69.9 70.1 70.0 68.3 68.4 75.0 75.4 68.1 68.4 75.0 75.4 74.7 74.6 74.5 74.7 74.3 74.4 75.5 |","cbCaijwZXcaYdtBj","https://ap.wps.com/l/cbCaijwZXcaYdtBj","pdf",3000712,9,"English","# Experimental Results\n## Depth and Silhouette Comparison\n## Model Accuracy and Baselines\n## Viewpoint-wise Error and IoU","[{\"question\":\"What inputs are modeled to synthesize 3D shapes in this work?\",\"answer\":\"The document models multi-view depth maps and silhouettes as the core representations for 3D shape synthesis.\"},{\"question\":\"How do unconditional and conditional settings affect Depth and Silhouette performance?\",\"answer\":\"It compares unconditional versus conditional scores for both Depth and Silhouette, showing measurable differences in the reported accuracy-related metrics.\"},{\"question\":\"Which networks are compared and how are they categorized by view usage?\",\"answer\":\"The document compares AllVPNet, DropoutNet, and SingleVPNet, distinguishing multi-view methods from a single-view approach, and also includes baselines such as MVCNN, VoxNet, 3D ShapeNet, and DeepPano.\"}]","Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks | PDF"]