MVP: One-Shot Object Pose Estimation by Matching With Visible Points

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-10-03 DOI:10.1109/LSP.2024.3472492
Wentao Cheng;Minxing Luo
{"title":"MVP: One-Shot Object Pose Estimation by Matching With Visible Points","authors":"Wentao Cheng;Minxing Luo","doi":"10.1109/LSP.2024.3472492","DOIUrl":null,"url":null,"abstract":"We introduce a novel method for one-shot object pose estimation. Recent detector-free one-shot methods have achieved promising results for challenging low-textured objects. The features in a query image are directly matched with all features in an object point cloud reconstructed via Structure-from-Motion (SfM) techniques. Rejecting invisible 3D points, as well as associated features, is performed implicitly using a deep neural network that is trained specifically for feature matching. This tightly-coupled strategy is prone to preserve 3D points that are rarely visible from the query view. In contrast, we propose to prune such erroneous points using the explicit image-point relational graph, which is a lightweight by-product of the SfM reconstruction. By injecting the graph-based pruning into stacked feature transformers, our method is able to obtain high quality 2D-3D correspondences through matching with visible points in an early stage. The experiments demonstrate that our method outperforms state-of-the-art model-free one-shot methods with faster speed.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Signal Processing Letters","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10705059/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

We introduce a novel method for one-shot object pose estimation. Recent detector-free one-shot methods have achieved promising results for challenging low-textured objects. The features in a query image are directly matched with all features in an object point cloud reconstructed via Structure-from-Motion (SfM) techniques. Rejecting invisible 3D points, as well as associated features, is performed implicitly using a deep neural network that is trained specifically for feature matching. This tightly-coupled strategy is prone to preserve 3D points that are rarely visible from the query view. In contrast, we propose to prune such erroneous points using the explicit image-point relational graph, which is a lightweight by-product of the SfM reconstruction. By injecting the graph-based pruning into stacked feature transformers, our method is able to obtain high quality 2D-3D correspondences through matching with visible points in an early stage. The experiments demonstrate that our method outperforms state-of-the-art model-free one-shot methods with faster speed.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
MVP:通过与可见点匹配进行单次物体姿态估计
我们介绍了一种一次性物体姿态估计的新方法。最近的免检测器单次拍摄方法在处理具有挑战性的低纹理物体方面取得了可喜的成果。查询图像中的特征与通过运动结构(SfM)技术重建的物体点云中的所有特征直接匹配。剔除不可见的三维点以及相关特征,是通过专门为特征匹配而训练的深度神经网络隐式执行的。这种紧密耦合的策略容易保留从查询视图中很少可见的三维点。相比之下,我们建议使用显式图像-点关系图(SfM 重构的轻量级副产品)来修剪此类错误点。通过将基于图的修剪注入堆叠特征变换器,我们的方法能够在早期通过与可见点匹配获得高质量的 2D-3D 对应。实验证明,我们的方法以更快的速度超越了最先进的无模型单次方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
期刊最新文献
KFA: Keyword Feature Augmentation for Open Set Keyword Spotting RFI-Aware and Low-Cost Maximum Likelihood Imaging for High-Sensitivity Radio Telescopes Audio Mamba: Bidirectional State Space Model for Audio Representation Learning System-Informed Neural Network for Frequency Detection Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1