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

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 All 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 Pseudo-label Generation (Infra-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 website at https: //www.nec-labs.com/mas/MM-TTA

Automated summary

This paper proposes a new multi-modal test-time adaptation approach for 3D semantic segmentation. It includes two modules, intra-modal pseudo-label generation and inter-modal pseudo-label refinement, which provide stable self-learning signals in various multi-modal test-time adaptation scenarios.