In this paper, we propose a novel Point Spatial-Temporal Transformer (P ST 2) network to tackle the above two challenges. First, we introduce a self-attention based module, i.e., Spatio-Temporal Self-Attention (STSA), to capture inter-frame spatial-temporal context informat...
Second, we uniquely use a point transformer network as an encoder to extract point feature information from bitemporal 3D point clouds. Then, we design a module for fusing the spatiotemporal features of bi-temporal point clouds to effectively detect change features. Finally, multilayer percep...
In this paper, we propose a novel framework named Point Spatial-Temporal Transformer (PST2) to learn spatial-temporal representations from dynamic 3D point cloud sequences. Our PST2 consists of two major modules: a Spatio-Temporal Self-Attention (STSA) module and a Resolution Embedding (RE) ...
几篇论文实现代码:《Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud Videos》(CVPR 2021) GitHub:https:// github.com/hehefan/P4Transformer [fig7] 《Adaptive Prototype Learn...
Specifically, P4Transformer consists of (i) a point 4D convolution to embed the spatio-temporal local structures presented in a point cloud video and (ii) a transformer to capture the appearance and motion information across the entire video by performing self-attention on the embedded local ...
To address this gap, this study introduces the Transformer-Based Neural Marked Spatio-Temporal Point Process (NMSTPP) model, specifically designed for football event data. The NMSTPP model predicts a comprehensive set of future event components, including inter-event time, zone, and action. ...
LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [det; Github] TopNet: Structural Point Cloud Decoder [completion; Github] FlowNet3D: Learning Scene Flow in 3D Point Clouds [scene flow; Tensorflow] Occupancy Networks: Learning 3D ...
Specifically, P4Transformer consists of (i) a point 4D convolution to embed the spatio-temporal local structures presented in a point cloud video and (ii) a transformer to capture the appearance and motion information across the entire video by performing self-attention on the embedded local ...
Inspired by the recent advances in vision domain, the point-based transformer is introduced to process the point cloud with the inherent permutation invariance characteristic. We develop Point Sampling Transformer Network (PST-NET), including data augmentation, self-attention and local feature extraction...
【论文阅读】Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation aggregationembeddinglayerpoipoint authors:: Xiaolin Wang, Guohao Sun, Xiu Fang, Jian Yang, Shoujin Wang container:: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence yea...