代码开源在https:// github.com/ dvlab-research/ StratifiedTransformer 每个点云视为一个token,编码器中的第一个模块中,采用point embedding来聚合局部信息(KPConv),point embedding的引入有利于加速网络收敛。 借鉴Swin Transformer的patch划分方法,将点云划分成多个不重叠的立体窗口,为进一步的提高感受野,引入了一种...
Vanilla Version Transformer(未分层):因为点云数据包含众多点的特性,当我们在全局对点云数据通过transformer块对数据进行处理的时候,会产生巨大的存储消耗(因为注意力机制需要输入全局所有点的信息)。故而,将3D空间分为许多个不重叠的独立的立体小个子,点云中的点散布在这些小格子中,然后单独在每一个格子中对点云数...
Point Transformer[62]使用 "向量自我注意 "算子来聚集局部特征,并使用 "减法关系 "来生成注意权重,但它存在缺乏长程上下文和测试中各种扰动时不够稳健的问题。我们的工作是基于指向性的,并与Transformer密切相关,但有一个根本的区别:我们的工作克服了有限的有效感受场问题,并充分利用Transformer来模拟长距离的语境依赖...
Stratified Transformer for 3D Point Cloud Segmentation Xin Lai*, Jianhui Liu*, Li Jiang, Liwei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia This is the official PyTorch implementation of our paperStratified Transformer for 3D Point Cloud Segmentationthat has been accepted to CVPR 2022...
3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies. In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong ...
POINT cloudTRANSFORMER modelsSOCIAL settlementsIMAGE segmentationIn recent years, semantic segmentation on 3D point cloud data has attracted much attention. Unlike 2D images where pixels distribute regularly in the image domain, 3D point clouds in non-Euclidean space are irregular a...