整个PointMAE行文的核心观点就是 leakage of location information(位置信息的泄漏) PointBERT pipeline Pipeline PointMAE pipeline 采用MAE的pipeline,在Encoder处仅输入visible tokens + visible token的positional embedding,在Decoder处才将visible tokens 和mask tokens 加上full-set的positional embedding 一起输入 采用...
mask掉一些token,剩下的输入到TransformerEncoder,位置信息的处理略 PointMAE 主类,就是论文的pipeline了。先用MaskTransformer,然后用TransformerDecoder。 都得提供mask map。 PointTransformer 用于下游任务的类,encoder接一个head。略
Point-MAE Masked Autoencoders for Point Cloud Self-supervised Learning, ECCV 2022, ArXiv In this work, we present a novel scheme of masked autoencoders for point cloud self-supervised learning, termed as Point-MAE. Our Point-MAE is neat and efficient, with minimal modifications based on the...
我们提出了Point-M2AE(如图1下半部分),首次将多尺度的3D空间信息引入MAE的预训练框架。我们设计了层次化的3D transformer结构,取代了2D MAE中编码相同图像分辨率的单尺度transformer。在encoder中,我们逐渐对点云特征进行下采样,使得网络可以由浅入深的学习到点云高层次的语义信息;在decoder中,我们反之进行上采样,并使...
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“MAE”和“Grade Point Scores”(GPS)是两个在教育领域中常见的术语,但它们代表的含义和应用场景有所不同。以下是对这两个概念的详细解释: MAE(Mean Absolute Error) 定义: MAE是统计学中的一个概念,全称为平均绝对误差(Mean Absolute Error)。它用于衡量预测值与实际观测值之间的偏差大小,具体来说,是所有单个...
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Based on this key observation, we propose a simple yet effective method, i.e., learning to Predict Centers for Point Masked AutoEncoders (PCP-MAE) which guides the model to learn to predict the significant centers and use the predicted centers to replace the directly provided centers. ...
Masked Autoencoders (MAE) have shown promising performance in self-supervised learning for both 2D and 3D computer vision. However, existing MAE-style methods can only learn from the data of a single modality, i.e., either images or point clouds, which neglect the implicit semantic and geomet...