{"title":"View-aligned pixel-level feature aggregation for 3D shape classification","authors":"","doi":"10.1016/j.cviu.2024.104098","DOIUrl":null,"url":null,"abstract":"<div><p>Multi-view 3D shape classification, which identifies a 3D shape based on its 2D views rendered from different viewpoints, has emerged as a promising method of shape understanding. A key building block in these methods is cross-view feature aggregation. However, existing methods dominantly follow the “extract-then-aggregate” pipeline for view-level global feature aggregation, leaving cross-view pixel-level feature interaction under-explored. To tackle this issue, we develop a “fuse-while-extract” pipeline, with a novel View-aligned Pixel-level Fusion (VPF) module to fuse cross-view pixel-level features originating from the same 3D part. We first reconstruct the 3D coordinate of each feature via the rasterization results, then match and fuse the features via spatial neighbor searching. Incorporating the proposed VPF module with ResNet18 backbone, we build a novel view-aligned multi-view network, which conducts feature extraction and cross-view fusion alternatively. Extensive experiments have demonstrated the effectiveness of the VPF module as well as the excellent performance of the proposed network.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224001796","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}
引用次数: 0
Abstract
Multi-view 3D shape classification, which identifies a 3D shape based on its 2D views rendered from different viewpoints, has emerged as a promising method of shape understanding. A key building block in these methods is cross-view feature aggregation. However, existing methods dominantly follow the “extract-then-aggregate” pipeline for view-level global feature aggregation, leaving cross-view pixel-level feature interaction under-explored. To tackle this issue, we develop a “fuse-while-extract” pipeline, with a novel View-aligned Pixel-level Fusion (VPF) module to fuse cross-view pixel-level features originating from the same 3D part. We first reconstruct the 3D coordinate of each feature via the rasterization results, then match and fuse the features via spatial neighbor searching. Incorporating the proposed VPF module with ResNet18 backbone, we build a novel view-aligned multi-view network, which conducts feature extraction and cross-view fusion alternatively. Extensive experiments have demonstrated the effectiveness of the VPF module as well as the excellent performance of the proposed network.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems