{"id":226,"date":"2024-05-12T23:29:43","date_gmt":"2024-05-12T23:29:43","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team10\/?page_id=226"},"modified":"2024-12-14T04:50:00","modified_gmt":"2024-12-14T04:50:00","slug":"results-aviral-agrawal","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team10\/results-aviral-agrawal\/","title":{"rendered":"Results"},"content":{"rendered":"
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Fig9. Results obtained from baseline models and Clear-Splatting (ours) approach<\/figcaption><\/figure>\n<\/div>\n\n
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We compare ClearSplatting-2.0 against Dex-NeRF, Res-NeRF, 3DGS, and ClearSplatting by computing the MAE and RMSE (figure 10).<\/p>\n

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Here i \u2208 [0,…,N] is the frame number, r is the pixel location, and \u03a9r is the set of all pixel locations across frames. D\u02c6(r) is the inferred depth in meters, D(r) is the GT depth in meters. We crop each image before evaluation to focus on the transparent object and not bias the results with the background.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n

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Fig10. Metrics used for quantitative evaluation<\/figcaption><\/figure>\n<\/div>\n\n

The objective results based on the metrics, RMSE and MAE, can be found in the tables below.<\/p>\n\n\n

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Fig 10. Depth RMSE results<\/figcaption><\/figure>\n\n\n\n
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Fig 11. Depth MSE results<\/figcaption><\/figure>\n\n\n

From Figure 9, 10, and 11, we see that Clear-Splatting outperforms NeRF-based baselines as well as other 3DGS-based baselines. The results suggest that Clear-Splatting improves on the NeRF-based approaches with a 67.09%<\/strong> lower RMSE and an 87.80%<\/strong> lower MAE in depth estimation. ClearSplatting-2.0 beats Clear-Splatting by upto 33%<\/strong> lower RMSE and by upto 32%<\/strong> lower MSE in depth estimation.<\/p>\n

From Figure 9, we see that the depth maps obtained using NeRF approaches do not have crisp boundaries in contrast to those obtained using 3DGS, ClearSplatting, and ClearSplatting-2.0. Furthermore, the depth maps obtained from baseline 3DGS has holes which can potentially lead to incorrect gripper pose estimation. ClearSplatting-2.0 is able to considerably close the holes obtained in the baselines and consequently lead to better gripper pose estimation. \u00a0<\/p>","protected":false},"excerpt":{"rendered":"

We compare ClearSplatting-2.0 against Dex-NeRF, Res-NeRF, 3DGS, and ClearSplatting by computing the MAE and RMSE (figure 10). Here i \u2208 [0,…,N] is the frame number, r is the pixel location, and \u03a9r is the set of all pixel locations across frames. D\u02c6(r) is the inferred depth in meters, D(r) is the GT depth in meters. … <\/p>\n

Continue reading “Results”<\/span><\/a><\/p>\n","protected":false},"author":210,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-226","page","type-page","status-publish","hentry"],"yoast_head":"\nResults - MS Computer Vision Capstone | Team 10<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2024team10\/results-aviral-agrawal\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Results - MS Computer Vision Capstone | Team 10\" \/>\n<meta property=\"og:description\" content=\"We compare ClearSplatting-2.0 against Dex-NeRF, Res-NeRF, 3DGS, and ClearSplatting by computing the MAE and RMSE (figure 10). 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