Data diversity and abundance are essential for improving the performance and
generalization of models in natural language processing and 2D vision. However,
3D vision domain suffers from the lack of 3D data, and simply combining
multiple 3D datasets for pretraining a 3D backbone does not yield significant
improvement, due to the domain discrepancies among different 3D datasets that
impede effective feature learning. In this work, we identify the main sources
of the domain discrepancies between 3D indoor scene datasets, and propose
Swin3D++, an enhanced architecture based on Swin3D for efficient pretraining on
multi-source 3D point clouds. Swin3D++ introduces domain-specific mechanisms to
Swin3D's modules to address domain discrepancies and enhance the network
capability on multi-source pretraining. Moreover, we devise a simple
source-augmentation strategy to increase the pretraining data scale and
facilitate supervised pretraining. We validate the effectiveness of our design,
and demonstrate that Swin3D++ surpasses the state-of-the-art 3D pretraining
methods on typical indoor scene understanding tasks. Our code and models will
be released at this https URL

在本研究中，我们发现了 3D 室内场景数据集之间的领域差异的主要来源，并提出了基于 Swin3D 的增强架构 Swin3D++，用于对多源 3D 点云进行高效预训练。Swin3D++ 引入了领域特定机制来解决领域差异，并增强了在多源预训练中的网络能力。此外，我们设计了一种简单的源增强策略来增加预训练数据规模并促进监督预训练。我们验证了我们的设计的有效性，并展示了 Swin3D++ 在典型室内场景理解任务上超越了最先进的 3D 预训练方法。

Swin3D++：3D 室内场景理解的有效多源预训练

Swin3D++: Effective Multi-Source Pretraining for 3D Indoor Scene  Understanding

Imitation learning seeks to circumvent the difficulty in designing proper
reward functions for training agents by utilizing expert behavior. With
environments modeled as Markov Decision Processes (MDP), most of the existing
imitation algorithms are contingent on the availability of expert
demonstrations in the same MDP as the one in which a new imitation policy is to
be learned. In this paper, we study the problem of how to imitate tasks when
there exist discrepancies between the expert and agent MDP. These discrepancies
across domains could include differing dynamics, viewpoint, or morphology; we
present a novel framework to learn correspondences across such domains.
Importantly, in contrast to prior works, we use unpaired and unaligned
trajectories containing only states in the expert domain, to learn this
correspondence. We utilize a cycle-consistency constraint on both the state
space and a domain agnostic latent space to do this. In addition, we enforce
consistency on the temporal position of states via a normalized position
estimator function, to align the trajectories across the two domains. Once this
correspondence is found, we can directly transfer the demonstrations on one
domain to the other and use it for imitation. Experiments across a wide variety
of challenging domains demonstrate the efficacy of our approach.

本篇研究针对专家行为与训练代理之间的差异，提出了一种基于无配对无对齐的轨迹，以及循环一致性限制的框架，来学习对应关系以解决领域差异的问题，并通过实验证明了该方法的有效性。