{"title":"三维远程沉浸视觉延迟估计","authors":"S. Raghuraman, K. Bahirat, B. Prabhakaran","doi":"10.1145/3083187.3084019","DOIUrl":null,"url":null,"abstract":"3D Tele-Immersion systems allow geographically distributed users to interact in a virtual world using their \"live\" 3D models. The capture, reconstruction, transfer, and rendering of these models introduce significant latency into the system. Implicit Latency (ℒ') can be estimated using system clocks to measure the time after the data was received from the RGB-D camera, till the request to render the result. The Observed Latency (ℒ) between a real world event and the event being rendered on the display, cannot be accurately represented by ℒ' since ℒ' ignores the time taken to capture, or update the display, etc. In this paper, a Visual Pattern based Latency Estimation (VPLE) approach is introduced to calculate the real world visual latency of a system without the need for any custom hardware. VPLE generates a constantly changing pattern that is captured and rendered by the 3DTI system. An external observer records both the pattern and the rendered results at high frame rates. ℒ is estimated by calculating the difference between the generated and rendered patterns. VPLE is extended to allow ℒ estimation between geographically distributed sites. Evaluations show that the accuracy of VPLE depends on the refresh rate of the pattern, and is within 4ms. ℒ of a distributed 3DTI system implemented on the GPU is significantly lower than the CPU implementation, and is comparable to video streaming. It is also shown that the ℒ' estimates for GPU based 3DTI implementations are off by almost 100% compared to the ℒ.","PeriodicalId":123321,"journal":{"name":"Proceedings of the 8th ACM on Multimedia Systems Conference","volume":"58 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Visual Latency Estimator for 3D Tele-Immersion\",\"authors\":\"S. Raghuraman, K. Bahirat, B. Prabhakaran\",\"doi\":\"10.1145/3083187.3084019\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"3D Tele-Immersion systems allow geographically distributed users to interact in a virtual world using their \\\"live\\\" 3D models. The capture, reconstruction, transfer, and rendering of these models introduce significant latency into the system. Implicit Latency (ℒ') can be estimated using system clocks to measure the time after the data was received from the RGB-D camera, till the request to render the result. The Observed Latency (ℒ) between a real world event and the event being rendered on the display, cannot be accurately represented by ℒ' since ℒ' ignores the time taken to capture, or update the display, etc. In this paper, a Visual Pattern based Latency Estimation (VPLE) approach is introduced to calculate the real world visual latency of a system without the need for any custom hardware. VPLE generates a constantly changing pattern that is captured and rendered by the 3DTI system. An external observer records both the pattern and the rendered results at high frame rates. ℒ is estimated by calculating the difference between the generated and rendered patterns. VPLE is extended to allow ℒ estimation between geographically distributed sites. Evaluations show that the accuracy of VPLE depends on the refresh rate of the pattern, and is within 4ms. ℒ of a distributed 3DTI system implemented on the GPU is significantly lower than the CPU implementation, and is comparable to video streaming. It is also shown that the ℒ' estimates for GPU based 3DTI implementations are off by almost 100% compared to the ℒ.\",\"PeriodicalId\":123321,\"journal\":{\"name\":\"Proceedings of the 8th ACM on Multimedia Systems Conference\",\"volume\":\"58 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM on Multimedia Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3083187.3084019\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th ACM on Multimedia Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3083187.3084019","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
3D Tele-Immersion systems allow geographically distributed users to interact in a virtual world using their "live" 3D models. The capture, reconstruction, transfer, and rendering of these models introduce significant latency into the system. Implicit Latency (ℒ') can be estimated using system clocks to measure the time after the data was received from the RGB-D camera, till the request to render the result. The Observed Latency (ℒ) between a real world event and the event being rendered on the display, cannot be accurately represented by ℒ' since ℒ' ignores the time taken to capture, or update the display, etc. In this paper, a Visual Pattern based Latency Estimation (VPLE) approach is introduced to calculate the real world visual latency of a system without the need for any custom hardware. VPLE generates a constantly changing pattern that is captured and rendered by the 3DTI system. An external observer records both the pattern and the rendered results at high frame rates. ℒ is estimated by calculating the difference between the generated and rendered patterns. VPLE is extended to allow ℒ estimation between geographically distributed sites. Evaluations show that the accuracy of VPLE depends on the refresh rate of the pattern, and is within 4ms. ℒ of a distributed 3DTI system implemented on the GPU is significantly lower than the CPU implementation, and is comparable to video streaming. It is also shown that the ℒ' estimates for GPU based 3DTI implementations are off by almost 100% compared to the ℒ.