{"title":"DeepLux:一种数据驱动的3D照度图估计方法和基准","authors":"T. Tsesmelis, N. Carissimi, Alessio Del Bue","doi":"10.1177/14771535221148736","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a pipeline and benchmark, called DeepLux, for predicting illuminance on 3D point clouds. Classic algorithms for computing photometrically accurate illumination are based on numerical and analytical models which are generally computationally expensive, especially in scenarios with complex geometries. Unlike existing approaches, our algorithm is the first learning-based method that is able to predict accurate illuminance map information that could be used for real-time smart lighting applications. We also evaluate our approach on two complementary tasks, that is, light position and intensity estimation, which are important aspects in the field of lighting design. Additionally, we provide an extensive novel photometrically correct dataset, which we use for training and evaluating our approach. Experiments show that the proposed algorithm produces results on par with or even better than the state of the art (+8% average error in real tests) while achieving faster simulation timings than its analytical counterpart, especially in complex synthetic and real-world scenarios.","PeriodicalId":18133,"journal":{"name":"Lighting Research & Technology","volume":"41 1","pages":"321 - 338"},"PeriodicalIF":2.1000,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DeepLux: A data-driven method and benchmark for 3D illuminance maps estimation\",\"authors\":\"T. Tsesmelis, N. Carissimi, Alessio Del Bue\",\"doi\":\"10.1177/14771535221148736\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose a pipeline and benchmark, called DeepLux, for predicting illuminance on 3D point clouds. Classic algorithms for computing photometrically accurate illumination are based on numerical and analytical models which are generally computationally expensive, especially in scenarios with complex geometries. Unlike existing approaches, our algorithm is the first learning-based method that is able to predict accurate illuminance map information that could be used for real-time smart lighting applications. We also evaluate our approach on two complementary tasks, that is, light position and intensity estimation, which are important aspects in the field of lighting design. Additionally, we provide an extensive novel photometrically correct dataset, which we use for training and evaluating our approach. Experiments show that the proposed algorithm produces results on par with or even better than the state of the art (+8% average error in real tests) while achieving faster simulation timings than its analytical counterpart, especially in complex synthetic and real-world scenarios.\",\"PeriodicalId\":18133,\"journal\":{\"name\":\"Lighting Research & Technology\",\"volume\":\"41 1\",\"pages\":\"321 - 338\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Lighting Research & Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/14771535221148736\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Lighting Research & Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/14771535221148736","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
DeepLux: A data-driven method and benchmark for 3D illuminance maps estimation
In this paper, we propose a pipeline and benchmark, called DeepLux, for predicting illuminance on 3D point clouds. Classic algorithms for computing photometrically accurate illumination are based on numerical and analytical models which are generally computationally expensive, especially in scenarios with complex geometries. Unlike existing approaches, our algorithm is the first learning-based method that is able to predict accurate illuminance map information that could be used for real-time smart lighting applications. We also evaluate our approach on two complementary tasks, that is, light position and intensity estimation, which are important aspects in the field of lighting design. Additionally, we provide an extensive novel photometrically correct dataset, which we use for training and evaluating our approach. Experiments show that the proposed algorithm produces results on par with or even better than the state of the art (+8% average error in real tests) while achieving faster simulation timings than its analytical counterpart, especially in complex synthetic and real-world scenarios.
期刊介绍:
Lighting Research & Technology (LR&T) publishes original peer-reviewed research on all aspects of light and lighting and is published in association with The Society of Light and Lighting. LR&T covers the human response to light, the science of light generation, light control and measurement plus lighting design for both interior and exterior environments, as well as daylighting, energy efficiency and sustainability