.png){kind=icon}
返回
Point Transformer
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3116304.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this work, we present Point Transformer, a deep neural network that operates directly on unordered and unstructured point sets. We design Point Transformer to extract local and global features and relate both representations by introducing the local-global attention mechanism, which aims to capture spatial point relations and shape information. For that purpose, we propose SortNet, as part of the Point Transformer, which induces input permutation invariance by selecting points based on a learned score. The output of Point Transformer is a sorted and permutation invariant feature list that can directly be incorporated into common computer vision applications. We evaluate our approach on standard classification and part segmentation benchmarks to demonstrate competitive results compared to the prior work.
Keyword:
Shape
Transformers
Three-dimensional displays
Standards
Task analysis
Feature extraction
Computer vision
3D point processing
artificial neural networks
computer vision
feedforward neural networks
transformer
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Lösliche
trans
‐Di‐1‐alkinyl‐ und Poly‐
trans
‐ethinyl(tetraalkylphthalocyaninato)metall‐IVB‐DerivateLösliche
trans
-二-1-炔基-与聚-
trans
-乙炔基 (四烷基酞菁) 金属-ⅳ-衍生物
PREAI
分享收藏Investigation of Ti mesh-supported anodes for direct borohydride fuel cells直接硼氢化物燃料电池用钛网支撑阳极的研究
PREAI
分享收藏
查看更多内容