point-wise Attention

point-wise Attention是一种注意力机制，用于判断一个体素中哪些点更加重要。在点云数据处理中，可以通过对每个点的特征进行maxpooling操作，得到一个channel-wise的特征表示。然后，通过使用ReLU激活函数，可以得到最终的point-wise attention参数，用于确定每个点的重要性。这样可以提取出关键的点特征，用于对象的判别表示。\[2\]\[3\]
#### 引用[.reference_title]
- *1* *2* *3* [【3D 目标检测】TANet: Robust 3D Object Detection from Point Clouds with Triple Attention](https://blog.csdn.net/qq_36380978/article/details/120692096)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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相关问题

point wise Attention

Point-wise Attention是Triple Attention模块中的一部分，用于判断一个体素中哪些点更加重要。它通过学习三个多层感知机（MLP）来实现，类似于之前提到的点注意力。Point-wise Attention考虑了不同点之间的影响，并将其加入到特征表示中。在Triple Attention模块中，Point-wise Attention与Channel-wise Attention和Voxel-wise Attention一起工作，以增强目标的关键信息并抑制不稳定的点。通过联合考虑通道注意力、点注意力和体素注意力，并执行堆叠操作，Triple Attention模块可以获得多级特征注意力，从而得到对象的判别表示。\[1\]\[2\]\[3\]
#### 引用[.reference_title]
- *1* *2* [【3D 目标检测】TANet: Robust 3D Object Detection from Point Clouds with Triple Attention](https://blog.csdn.net/qq_36380978/article/details/120692096)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [[点云分割]-Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds](https://blog.csdn.net/alfred_torres/article/details/107700798)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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Point Transformer

Point Transformer是一个在点云处理中的应用，它是整个网络的主要特征聚合算子。网络架构包括Point Transformer layers、pointwise transformations和pooling。Point Transformer Block是该网络的核心组件，它基于vector self-attention，并使用减法关系将位置编码加到注意向量和变换特征上。该研究探究了Transformer在点云处理中的应用，并在场景分割、目标分类和语义分割等任务上取得了很不错的效果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [「论文阅读」Point Transformer（ICCV_2021_paper_Zhao）](https://blog.csdn.net/u013609289/article/details/122906946)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *3* [【论文阅读】Point Transformer解读](https://blog.csdn.net/weixin_41317766/article/details/119852644)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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