Chen Lv , Chenggong Han , Jochen Lang , He Jiang , Deqiang Cheng , Jiansheng Qian
{"title":"GDM-depth:利用全局依赖性建模进行自监督室内深度估算","authors":"Chen Lv , Chenggong Han , Jochen Lang , He Jiang , Deqiang Cheng , Jiansheng Qian","doi":"10.1016/j.imavis.2024.105160","DOIUrl":null,"url":null,"abstract":"<div><p>Self-supervised depth estimation algorithms eschew depth ground truth and employ the convolutional U-Net with a fixed receptive field which confines its focus primarily to nearby spatial distances. These factors obscure adequate supervision during image reconstruction, consequently hindering accurate depth estimation, particularly in complex indoor scenes. The pure transformer framework can perform global modelling to provide more semantic information. However, the cost is significant. To tackle these challenges, we introduce GDM-Depth, which utilizes global dependency modelling to offer more precise depth guidance from the network itself. Initially, we propose integrating learnable tree filters with unary terms, leveraging the structural properties of spanning trees to facilitate efficient long-range interactions. Subsequently, instead of replacing the convolutional framework entirely, we employ the transformer to design a scale-aware global feature extractor, establishing global relationships among local features at various scales, achieving both efficiency and cost-effectiveness. Furthermore, inter-class disparities between depth global and local features are observed. To address this issue, we introduce the global feature injector to further enhance the representation. GDM-Depth's effectiveness is demonstrated on the NYUv2, ScanNet, and InteriorNet depth datasets, achieving impressive test set performances of 87.2%, 83.1%, and 76.1% in key indicators <span><math><mi>δ</mi><mo><</mo><mn>0.125</mn></math></span>, respectively.</p></div>","PeriodicalId":50374,"journal":{"name":"Image and Vision Computing","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"GDM-depth: Leveraging global dependency modelling for self-supervised indoor depth estimation\",\"authors\":\"Chen Lv , Chenggong Han , Jochen Lang , He Jiang , Deqiang Cheng , Jiansheng Qian\",\"doi\":\"10.1016/j.imavis.2024.105160\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Self-supervised depth estimation algorithms eschew depth ground truth and employ the convolutional U-Net with a fixed receptive field which confines its focus primarily to nearby spatial distances. These factors obscure adequate supervision during image reconstruction, consequently hindering accurate depth estimation, particularly in complex indoor scenes. The pure transformer framework can perform global modelling to provide more semantic information. However, the cost is significant. To tackle these challenges, we introduce GDM-Depth, which utilizes global dependency modelling to offer more precise depth guidance from the network itself. Initially, we propose integrating learnable tree filters with unary terms, leveraging the structural properties of spanning trees to facilitate efficient long-range interactions. Subsequently, instead of replacing the convolutional framework entirely, we employ the transformer to design a scale-aware global feature extractor, establishing global relationships among local features at various scales, achieving both efficiency and cost-effectiveness. Furthermore, inter-class disparities between depth global and local features are observed. To address this issue, we introduce the global feature injector to further enhance the representation. GDM-Depth's effectiveness is demonstrated on the NYUv2, ScanNet, and InteriorNet depth datasets, achieving impressive test set performances of 87.2%, 83.1%, and 76.1% in key indicators <span><math><mi>δ</mi><mo><</mo><mn>0.125</mn></math></span>, respectively.</p></div>\",\"PeriodicalId\":50374,\"journal\":{\"name\":\"Image and Vision Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Image and Vision Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0262885624002658\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Image and Vision Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0262885624002658","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
GDM-depth: Leveraging global dependency modelling for self-supervised indoor depth estimation
Self-supervised depth estimation algorithms eschew depth ground truth and employ the convolutional U-Net with a fixed receptive field which confines its focus primarily to nearby spatial distances. These factors obscure adequate supervision during image reconstruction, consequently hindering accurate depth estimation, particularly in complex indoor scenes. The pure transformer framework can perform global modelling to provide more semantic information. However, the cost is significant. To tackle these challenges, we introduce GDM-Depth, which utilizes global dependency modelling to offer more precise depth guidance from the network itself. Initially, we propose integrating learnable tree filters with unary terms, leveraging the structural properties of spanning trees to facilitate efficient long-range interactions. Subsequently, instead of replacing the convolutional framework entirely, we employ the transformer to design a scale-aware global feature extractor, establishing global relationships among local features at various scales, achieving both efficiency and cost-effectiveness. Furthermore, inter-class disparities between depth global and local features are observed. To address this issue, we introduce the global feature injector to further enhance the representation. GDM-Depth's effectiveness is demonstrated on the NYUv2, ScanNet, and InteriorNet depth datasets, achieving impressive test set performances of 87.2%, 83.1%, and 76.1% in key indicators , respectively.
期刊介绍:
Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.