亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Point Tree Transformer for Point Cloud Registration

计算机科学 点云 云计算 计算机视觉 人工智能 操作系统
作者
Meiling Wang,Guangyan Chen,Yi Yang,Li Yuan,Yufeng Yue
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology [Institute of Electrical and Electronics Engineers]
卷期号:: 1-1
标识
DOI:10.1109/tcsvt.2025.3526680
摘要

Point cloud registration is a fundamental task in the fields of computer vision and robotics. Recent advancements in transformer-based methods have demonstrated enhanced performance in this domain. However, the standard attention mechanisms employed in these approaches tend to incorporate numerous points of low relevance, and therefore struggle to focus their attention weights on sparse yet meaningful points. This inefficiency leads to limited local structure modeling capabilities and quadratic computational complexity. To overcome these limitations, we propose the Point Tree Transformer (PTT), a novel transformer-based approach for point cloud registration that efficiently extracts comprehensive local and global features while maintaining linear computational complexity. The PTT constructs hierarchical feature trees from point clouds in a coarse-to-dense manner, and introduces a novel Point Tree Attention (PTA) mechanism. This mechanism adheres to the tree structure to facilitate the progressive convergence of attended regions toward salient points. Specifically, each tree layer selectively identifies a subset of relevant points with the highest attention scores, and subsequent layers focus attention on areas of significant relevance, derived from the child points of the selected point set. The feature extraction process additionally incorporates coarse point features that capture high-level semantic information, thus facilitating local structure modeling and the progressive integration of multiscale information. Consequently, the PTA enables the model to focus on essential local structures and extract intricate local information while maintaining linear computational complexity. Extensive experiments conducted on the 3DMatch, ModelNet40, and KITTI datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The code for our method is publicly available at https://github.com/CGuangyan-BIT/PTT.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
科研通AI2S应助科研通管家采纳,获得10
20秒前
科研通AI2S应助科研通管家采纳,获得10
20秒前
ICSSCI完成签到,获得积分10
25秒前
38秒前
董可以发布了新的文献求助10
42秒前
风华正茂完成签到,获得积分10
1分钟前
1分钟前
1分钟前
jimmy_bytheway完成签到,获得积分0
1分钟前
桃桃发布了新的文献求助10
1分钟前
可爱的函函应助桃桃采纳,获得10
1分钟前
1分钟前
2分钟前
2分钟前
2分钟前
NexusExplorer应助科研通管家采纳,获得10
2分钟前
所所应助爱笑的毛衣采纳,获得10
2分钟前
2分钟前
2分钟前
duan完成签到 ,获得积分10
2分钟前
holder完成签到,获得积分10
3分钟前
3分钟前
沐白发布了新的文献求助10
3分钟前
3分钟前
刘宇童发布了新的文献求助10
3分钟前
大模型应助吕易巧采纳,获得10
3分钟前
迷人问兰完成签到,获得积分10
3分钟前
闪闪映易完成签到 ,获得积分10
3分钟前
3分钟前
吕易巧发布了新的文献求助10
3分钟前
吕易巧完成签到,获得积分10
3分钟前
4分钟前
Liiiiiiiiii发布了新的文献求助10
4分钟前
XuchaoD完成签到,获得积分10
4分钟前
4分钟前
今后应助Liiiiiiiiii采纳,获得10
4分钟前
顾矜应助科研通管家采纳,获得10
4分钟前
Akim应助科研通管家采纳,获得10
4分钟前
4分钟前
4分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990049
求助须知:如何正确求助?哪些是违规求助? 3532108
关于积分的说明 11256354
捐赠科研通 3270976
什么是DOI,文献DOI怎么找? 1805166
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809228