Test-time adaptation approaches have recently emerged as a practical solution
for handling domain shift without access to the source domain data. In this
paper, we propose and explore a new multi-modal extension of test-time
adaptation for 3D semantic segmentation. We find that directly applying
existing methods usually results in performance instability at test time
because multi-modal input is not considered jointly. To design a framework that
can take full advantage of multi-modality, where each modality provides
regularized self-supervisory signals to other modalities, we propose two
complementary modules within and across the modalities. First, Intra-modal
Pseudolabel Generation (Intra-PG) is introduced to obtain reliable pseudo
labels within each modality by aggregating information from two models that are
both pre-trained on source data but updated with target data at different
paces. Second, Inter-modal Pseudo-label Refinement (Inter-PR) adaptively
selects more reliable pseudo labels from different modalities based on a
proposed consistency scheme. Experiments demonstrate that our regularized
pseudo labels produce stable self-learning signals in numerous multi-modal
test-time adaptation scenarios for 3D semantic segmentation. Visit our project
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本文提出一种新的多模态测试时自适应模型，其中包含 Intra-PG 模块用于获取可靠的伪标签，以及 Inter-PR 模块用于自适应选择更可靠的伪标签，实现了基于多模态输入的 3D 语义分割稳定自学习。

MM-TTA: 3D 语义分割的多模态测试时适应

MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation

Most of the current scene flow methods choose to model scene flow as a per
point translation vector without differentiating between static and dynamic
components of 3D motion. In this work we present an alternative method for
end-to-end scene flow learning by joint estimation of non-rigid residual flow
and ego-motion flow for dynamic 3D scenes. We propose to learn the relative
rigid transformation from a pair of point clouds followed by an iterative
refinement. We then learn the non-rigid flow from transformed inputs with the
deducted rigid part of the flow. Furthermore, we extend the supervised
framework with self-supervisory signals based on the temporal consistency
property of a point cloud sequence. Our solution allows both training in a
supervised mode complemented by self-supervisory loss terms as well as training
in a fully self-supervised mode. We demonstrate that decomposition of scene
flow into non-rigid flow and ego-motion flow along with an introduction of the
self-supervisory signals allowed us to outperform the current state-of-the-art
supervised methods.

提出了一种端到端场景流学习的替代方法，通过联合估计非刚性残留流和动态 3D 场景的自运动流来学习场景流，引入了自监督信号以增强模型训练，能够将场景流分解为非刚性流和自运动流并且在当前最先进的监督方法上取得了更好的表现。