{"title":"3D- vrvt:基于视觉转换器的单幅图像三维体素重建","authors":"Xi Li, Ping Kuang","doi":"10.1109/ICCST53801.2021.00078","DOIUrl":null,"url":null,"abstract":"Deep CNN methods have shown very competitive performance in 3D voxel reconstruction from single-view synthetic clean-background images. However, how to generate the target object from a real-world image with clutter background is rarely studied. In this paper, we present a novel network named 3D-VRVT for 3D voxel reconstruction from a single image. Unlike pure CNN-based methods in the past, our 3D-VRVT extracts region features with Vision Transformer (ViT) encoder based on self-attention mechanism, and then a well-designed voxel decoder is used to generate three-dimensional voxel from the encoded image features. The experimental results show that our 3D-VRVT can reconstruct 3D voxel from both synthetic clean-background and real-world images effectively.","PeriodicalId":222463,"journal":{"name":"2021 International Conference on Culture-oriented Science & Technology (ICCST)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"3D-VRVT: 3D Voxel Reconstruction from A Single Image with Vision Transformer\",\"authors\":\"Xi Li, Ping Kuang\",\"doi\":\"10.1109/ICCST53801.2021.00078\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep CNN methods have shown very competitive performance in 3D voxel reconstruction from single-view synthetic clean-background images. However, how to generate the target object from a real-world image with clutter background is rarely studied. In this paper, we present a novel network named 3D-VRVT for 3D voxel reconstruction from a single image. Unlike pure CNN-based methods in the past, our 3D-VRVT extracts region features with Vision Transformer (ViT) encoder based on self-attention mechanism, and then a well-designed voxel decoder is used to generate three-dimensional voxel from the encoded image features. The experimental results show that our 3D-VRVT can reconstruct 3D voxel from both synthetic clean-background and real-world images effectively.\",\"PeriodicalId\":222463,\"journal\":{\"name\":\"2021 International Conference on Culture-oriented Science & Technology (ICCST)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Culture-oriented Science & Technology (ICCST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCST53801.2021.00078\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Culture-oriented Science & Technology (ICCST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCST53801.2021.00078","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
3D-VRVT: 3D Voxel Reconstruction from A Single Image with Vision Transformer
Deep CNN methods have shown very competitive performance in 3D voxel reconstruction from single-view synthetic clean-background images. However, how to generate the target object from a real-world image with clutter background is rarely studied. In this paper, we present a novel network named 3D-VRVT for 3D voxel reconstruction from a single image. Unlike pure CNN-based methods in the past, our 3D-VRVT extracts region features with Vision Transformer (ViT) encoder based on self-attention mechanism, and then a well-designed voxel decoder is used to generate three-dimensional voxel from the encoded image features. The experimental results show that our 3D-VRVT can reconstruct 3D voxel from both synthetic clean-background and real-world images effectively.