Point Transformers. Contribute to qq456cvb/Point-Transformers development by creating an account on GitHub.
Point Transformers. Contribute to qq456cvb/Point-Transformers development by creating an account on GitHub.
提出了Point-BERT,a new paradigm for learning Transformers将bert扩展到点云。受bert启发,设计了aMasked Point Modeling(MPM) task 预训练 point cloud Transformers。首先将点云分割为several local point patches,设计了一个带有discrete Variational AutoEncoder (dVAE)的a point cloud Tokenizer——生成discrete poin...
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling Point-BERT:基于掩码建模的点云自注意力模型预训练 摘要 我们提出了Point-BERT,一个学习自注意力的新范式,将BERT[8]的概念推广到三维点云。受BERT的启发,我们设计了一个掩蔽点建模(MPM)任务来预训练点云自注意力。具体来说,我们...
Point Transformers are near state-of-the-art models for classification, segmentation, and detection tasks on Point Cloud data. They utilize a self attention based mechanism to model large range spatial dependencies between multiple point sets. In this project we explore two things: classification perf...
因此,在预训练Point-BERT之前,需要构建一个类似于“词典”的东西。论文首先训练一个DiscreteVAE网络将每个group(一个中心点以及它的K近邻构成的点集合)映射到一个特定的类别(对应NLP里面的一个单词)。然后使用预训练好的DiscreteVAE为Point-BERT里面的group提供ground-truth标签。最后mask掉一个完成group序列里面的某些...
https://sites.google.com/view/pcn-cvpr24tutorial/homepageAll You Need To Know About Point Cloud Understandingppt:https://sites.google.com/view/pcn-cvpr24tutorial/homepage, 视频播放量 3、弹幕量 0、点赞数 0、投硬币枚数 0、收藏人数 2、转发人数 0, 视频作者
1 Apr 2024·Kartik Gupta,Rahul Vippala,Sahima Srivastava· Point Transformers are near state-of-the-art models for classification, segmentation, and detection tasks on Point Cloud data. They utilize a self attention based mechanism to model large range spatial dependencies between multiple point sets...
In this paper, we propose a simple yet effective approach for Tracking Any Point with TRansformers (TAPTR). Based on the observation that point tracking bears a great resemblance to object detection and tracking, we borrow designs from DETR-like algorithms to address the task of TAP. In TAP...
transformers to point clouds is reducing the quadratic, thus overwhelming, computation complexity of attentions. To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window. However, the point number in each window varies greatly, ...