Transfer learning has long been a key factor in the advancement of many
fields including 2D image analysis. Unfortunately, its applicability in 3D data
processing has been relatively limited. While several approaches for 3D
transfer learning have been proposed in recent literature, with contrastive
learning gaining particular prominence, most existing methods in this domain
have only been studied and evaluated in limited scenarios. Most importantly,
there is currently a lack of principled understanding of both when and why 3D
transfer learning methods are applicable. Remarkably, even the applicability of
standard supervised pre-training is poorly understood. In this work, we conduct
the first in-depth quantitative and qualitative investigation of supervised and
contrastive pre-training strategies and their utility in downstream 3D tasks.
We demonstrate that layer-wise analysis of learned features provides
significant insight into the downstream utility of trained networks. Informed
by this analysis, we propose a simple geometric regularization strategy, which
improves the transferability of supervised pre-training. Our work thus sheds
light onto both the specific challenges of 3D transfer learning, as well as
strategies to overcome them.

在本研究中，我们对监督学习和对比学习预训练策略及其在下游 3D 任务中的实用性进行了深入的定量和定性调查，通过分层分析学到的特征提供了对训练网络在下游任务中的可用性的重要见解，基于这一分析，我们提出了一种简单的几何正则化策略，来提高监督学习的可迁移性，我们的工作揭示了 3D 迁移学习的具体挑战以及克服这些挑战的策略。

监督与非监督：理解和解决 3D 迁移学习的关键挑战

To Supervise or Not to Supervise: Understanding and Addressing the Key  Challenges of 3D Transfer Learning

Processing 3D data efficiently has always been a challenge. Spatial
operations on large-scale point clouds, stored as sparse data, require extra
cost. Attracted by the success of transformers, researchers are using
multi-head attention for vision tasks. However, attention calculations in
transformers come with quadratic complexity in the number of inputs and miss
spatial intuition on sets like point clouds. We redesign set transformers in
this work and incorporate them into a hierarchical framework for shape
classification and part and scene segmentation. We propose our local attention
unit, which captures features in a spatial neighborhood. We also compute
efficient and dynamic global cross attentions by leveraging sampling and
grouping at each iteration. Finally, to mitigate the non-heterogeneity of point
clouds, we propose an efficient Multi-Scale Tokenization (MST), which extracts
scale-invariant tokens for attention operations. The proposed hierarchical
model achieves state-of-the-art shape classification in mean accuracy and
yields results on par with the previous segmentation methods while requiring
significantly fewer computations. Our proposed architecture predicts
segmentation labels with around half the latency and parameter count of the
previous most efficient method with comparable performance. The code is
available at this https URL.

使用集合转换器在分层框架中对点云进行形状分类和分割，实现了状态 - of-the-art 的性能，并可用于处理大规模稀疏数据。