In recent years, the field of 3D self-supervised learning has witnessed
significant progress, resulting in the emergence of Multi-Modality Masked
AutoEncoders (MAE) methods that leverage both 2D images and 3D point clouds for
pre-training. However, a notable limitation of these approaches is that they do
not fully utilize the multi-view attributes inherent in 3D point clouds, which
is crucial for a deeper understanding of 3D structures. Building upon this
insight, we introduce a novel approach employing a 3D to multi-view masked
autoencoder to fully harness the multi-modal attributes of 3D point clouds. To
be specific, our method uses the encoded tokens from 3D masked point clouds to
generate original point clouds and multi-view depth images across various
poses. This approach not only enriches the model's comprehension of geometric
structures but also leverages the inherent multi-modal properties of point
clouds. Our experiments illustrate the effectiveness of the proposed method for
different tasks and under different settings. Remarkably, our method
outperforms state-of-the-art counterparts by a large margin in a variety of
downstream tasks, including 3D object classification, few-shot learning, part
segmentation, and 3D object detection. Code will be available at:
this https URL

通过使用 3D 到多视图掩码自编码器，充分利用 3D 点云的多模属性，提出了一种全新的方法，这不仅丰富了模型对几何结构的理解，还利用了点云的固有多模性能，有效地改进了各种任务，包括 3D 对象分类、少样本学习、部分分割和 3D 对象检测。

点云自监督学习：基于三维到多视角遮蔽自编码器

Point Cloud Self-supervised Learning via 3D to Multi-view Masked  Autoencoder

Online movie review platforms are providing crowdsourced feedback for the
film industry and the general public, while spoiler reviews greatly compromise
user experience. Although preliminary research efforts were made to
automatically identify spoilers, they merely focus on the review content
itself, while robust spoiler detection requires putting the review into the
context of facts and knowledge regarding movies, user behavior on film review
platforms, and more. In light of these challenges, we first curate a
large-scale network-based spoiler detection dataset LCS and a comprehensive and
up-to-date movie knowledge base UKM. We then propose MVSD, a novel Multi-View
Spoiler Detection framework that takes into account the external knowledge
about movies and user activities on movie review platforms. Specifically, MVSD
constructs three interconnecting heterogeneous information networks to model
diverse data sources and their multi-view attributes, while we design and
employ a novel heterogeneous graph neural network architecture for spoiler
detection as node-level classification. Extensive experiments demonstrate that
MVSD advances the state-of-the-art on two spoiler detection datasets, while the
introduction of external knowledge and user interactions help ground robust
spoiler detection. Our data and code are available at
this https URL

提出了一种基于多视角和外部知识的电影评论剧透检测框架，MVSD，其中包含三种互联的异构信息网络和一个新颖的异构图神经网络架构，该框架在两个剧透检测数据集上实现了新的成果，并帮助地面化鲁棒的剧透检测。