{"title":"渐进式实时渲染未处理的点云","authors":"Markus Schütz, M. Wimmer","doi":"10.1145/3230744.3230816","DOIUrl":null,"url":null,"abstract":"Rendering tens of millions of points in real time usually requires either high-end graphics cards, or the use of spatial acceleration structures. We introduce a method to progressively display as many points as the GPU memory can hold in real time by reprojecting what was visible and randomly adding additional points to uniformly converge towards the full result within a few frames. Our method heavily limits the number of points that have to be rendered each frame and it converges quickly and in a visually pleasing way, which makes it suitable even for notebooks with low-end GPUs. The data structure consists of a randomly shuffled array of points that is incrementally generated on-the-fly while points are being loaded. Due to this, it can be used to directly view point clouds in common sequential formats such as LAS or LAZ while they are being loaded and without the need to generate spatial acceleration structures in advance, as long as the data fits into GPU memory.","PeriodicalId":226759,"journal":{"name":"ACM SIGGRAPH 2018 Posters","volume":"115 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Progressive real-time rendering of unprocessed point clouds\",\"authors\":\"Markus Schütz, M. Wimmer\",\"doi\":\"10.1145/3230744.3230816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rendering tens of millions of points in real time usually requires either high-end graphics cards, or the use of spatial acceleration structures. We introduce a method to progressively display as many points as the GPU memory can hold in real time by reprojecting what was visible and randomly adding additional points to uniformly converge towards the full result within a few frames. Our method heavily limits the number of points that have to be rendered each frame and it converges quickly and in a visually pleasing way, which makes it suitable even for notebooks with low-end GPUs. The data structure consists of a randomly shuffled array of points that is incrementally generated on-the-fly while points are being loaded. Due to this, it can be used to directly view point clouds in common sequential formats such as LAS or LAZ while they are being loaded and without the need to generate spatial acceleration structures in advance, as long as the data fits into GPU memory.\",\"PeriodicalId\":226759,\"journal\":{\"name\":\"ACM SIGGRAPH 2018 Posters\",\"volume\":\"115 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGGRAPH 2018 Posters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3230744.3230816\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGGRAPH 2018 Posters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3230744.3230816","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Progressive real-time rendering of unprocessed point clouds
Rendering tens of millions of points in real time usually requires either high-end graphics cards, or the use of spatial acceleration structures. We introduce a method to progressively display as many points as the GPU memory can hold in real time by reprojecting what was visible and randomly adding additional points to uniformly converge towards the full result within a few frames. Our method heavily limits the number of points that have to be rendered each frame and it converges quickly and in a visually pleasing way, which makes it suitable even for notebooks with low-end GPUs. The data structure consists of a randomly shuffled array of points that is incrementally generated on-the-fly while points are being loaded. Due to this, it can be used to directly view point clouds in common sequential formats such as LAS or LAZ while they are being loaded and without the need to generate spatial acceleration structures in advance, as long as the data fits into GPU memory.