[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-187729-en":3,"doc-seo-187729-105":30,"detail-sidebar-cat-1-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":11,"category_id":12,"category_name":13,"doc_title":14,"doc_description":15,"doc_content":16,"file_id":17,"file_url":18,"file_type":19,"file_size":20,"view_count":4,"is_deleted":4,"is_public":11,"is_downloadable":11,"audit_status":11,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":15,"update_tm":28,"read_time":29},187729,687207017582,"Ethan Miller","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",1,11,"Presentations","content","A set of experimental comparisons evaluates multiple methods for image quality and similarity. Metrics include PSNR (higher is better), SSIM (higher is better), and LPIPS (lower is better) across several benchmark settings such as S3-1K/S3-4K, S22-1K/S22-4K, and S2618 with different resolution or reconstruction regimes. Results consistently show that the proposed approach improves PSNR and SSIM while reducing LPIPS, with the larger variant delivering further gains. Additional ablation experiments analyze effects of removing components and varying view inputs, demonstrating performance sensitivity to key design choices and confirming the overall effectiveness of the proposed method.","| Method | PSNR ↑ | SSIM ↑ | LPIPS ↓ | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | S3-1K |  |  | S3-4K |  |  |\n| ENeRF [34] | 30.7611 | 0.8702 | 0.0943 | 28.7297 | 0.8462 | 0.1590 |\n| DVA [46] | 31.3487 | 0.8770 | 0.0910 | 29.3681 | 0.8503 | 0.1655 |\n| HoloChar [53] | 30.7795 | 0.8814 | 0.0841 | 28.9313 | 0.8620 | 0.1549 |\n| GHG [29] | 31.4086 | 0.8840 | 0.0907 | 29.2304 | 0.8537 | 0.1551 |\n| Ours | 32.8897 | 0.9057 | 0.0612 | 29.8369 | 0.8662 | 0.1389 |\n| Ours-Large | 33.1960 | 0.9113 | 0.0560 | 30.0311 | 0.8722 | 0.1322 |\n|  | S22-1K |  |  | S22-4K |  |  |\n| ENeRF [34] | 32.7524 | 0.8721 | 0.0853 | 30.2329 | 0.8325 | 0.1703 |\n| DVA [46] | 33.7842 | 0.8887 | 0.0664 | 31.2019 | 0.8448 | 0.1711 |\n| HoloChar [53] | 32.2055 | 0.8850 | 0.0775 | 30.1391 | 0.8533 | 0.1691 |\n| GHG [29] | 33.3187 | 0.8847 | 0.0846 | 30.8346 | 0.8448 | 0.1675 |\n| Ours | 34.1119 | 0.9066 | 0.0568 | 30.6427 | 0.8480 | 0.1387 |\n| Ours-Large | 34.2425 | 0.9113 | 0.0531 | 30.8126 | 0.8565 | 0.1290 |\n|  | S2618-Half Res |  |  | S2618-Full Res |  |  |\n| ENeRF [34] | 29.6588 | 0.8761 | 0.0989 | 27.6079 | 0.8439 | 0.1537 |\n| DVA [46] | 30.8135 | 0.8917 | 0.0774 | 29.2767 | 0.8611 | 0.1295 |\n| HoloChar [53] | 28.3811 | 0.8722 | 0.1132 | 28.1441 | 0.8666 | 0.1602 |\n| GHG [29] | 29.6830 | 0.8831 | 0.1059 | 28.4991 | 0.8597 | 0.1587 |\n| Ours | 31.2413 | 0.9101 | 0.0747 | 29.3799 | 0.8719 | 0.1355 |\n| Ours-Large | 31.5920 | 0.9179 | 0.0678 | 29.6887 | 0.8815 | 0.1235 |\n\n| Methods | Backbone | Tex Res | CD↓ | SR↓ | SI ↓ |\n| --- | --- | --- | --- | --- | --- |\n| DDC | GCN | - | 1.173 | 0.388 | 1.162 |\n| LBS | - | - | 2.117 | \u003Cbr>0.236 | \u003Cbr>0.768 |\n| w/o RGB\u003Cbr>w/o NML\u003Cbr>w/ 1 View\u003Cbr>w/ 2 View\u003Cbr>Ours | CNN\u003Cbr>CNN\u003Cbr>CNN\u003Cbr>CNN\u003Cbr>CNN | 256\u003Cbr>256\u003Cbr>256\u003Cbr>256\u003Cbr>256 | 1.150 1.117\u003Cbr>\u003Cbr>1.109 1.096\u003Cbr>1.070 |  0.360  0.387\u003Cbr>\u003Cbr>0.356\u003Cbr>0.365 0.374 | 0.895\u003Cbr>0.869\u003Cbr> 0.822 \u003Cbr>0.881\u003Cbr> 0.810  |\n\n\n| Methods | Tex Res | PSNR ↑ | SSIM ↑ | LPIPS ↓ |\n| --- | --- | --- | --- | --- |\n| w/ 0 View\u003Cbr>w/ 1 View\u003Cbr>w/ 2 View | 256\u003Cbr>256\u003Cbr>256 | 25.1546\u003Cbr>27.9061\u003Cbr>29.3016 | 0.7874\u003Cbr>0.8340\u003Cbr>0.8568 | 0.1848\u003Cbr>0.1579\u003Cbr>0.1436 |\n| w/ ‘w/o RGB Geo’\u003Cbr>w/ ‘w/o NML Geo’ | 256\u003Cbr>256 | 29.7097\u003Cbr>29.6742 | 0.8638\u003Cbr>0.8640 | 0.1402\u003Cbr>0.1397 |\n| w/o DU | 256 | 29.4185 | 0.8543 | 0.1452 |\n| w/o GSR | 256 |  29.8102  |  0.8657  |  0.1390  |\n| Ours | 256 |  29.8369  |  0.8662  |  0.1389  |\n| Ours-Large | 512 |  30.0311  |  0.8722  |  0.1322  |","cbCaivKy5hIQXpo7","https://ap.wps.com/l/cbCaivKy5hIQXpo7","pdf",10012367,12,"English","en",105,"# Experimental Results\n## Benchmark Tables\n## Ablation Studies","[{\"question\":\"Which evaluation metrics are reported in the document?\",\"answer\":\"PSNR and SSIM are reported as higher-is-better metrics, while LPIPS is reported as a lower-is-better metric.\"},{\"question\":\"What do the results suggest about the proposed method compared with baselines?\",\"answer\":\"Across multiple settings, the proposed method yields higher PSNR and SSIM and lower LPIPS than the compared methods, indicating improved reconstruction quality.\"},{\"question\":\"How do the ablation experiments assess design impact?\",\"answer\":\"The ablations remove specific components (e.g., RGB or NML variants, DU, GSR) and vary the number of views, then measure how PSNR/SSIM/LPIPS change to quantify each component’s contribution.\"}]","content | 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evaluation metrics are reported in the document?","Question",{"text":74,"@type":75},"PSNR and SSIM are reported as higher-is-better metrics, while LPIPS is reported as a lower-is-better metric.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What do the results suggest about the proposed method compared with baselines?",{"text":79,"@type":75},"Across multiple settings, the proposed method yields higher PSNR and SSIM and lower LPIPS than the compared methods, indicating improved reconstruction quality.",{"name":81,"@type":72,"acceptedAnswer":82},"How do the ablation experiments assess design impact?",{"text":83,"@type":75},"The ablations remove specific components (e.g., RGB or NML variants, DU, GSR) and vary the number of views, then measure how PSNR/SSIM/LPIPS change to quantify each component’s 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