Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud

• URL: http://arxiv.org/abs/2410.02720v2
• Date: Sat, 5 Oct 2024 03:11:37 GMT
• Title: Curvature Diversity-Driven Deformation and Domain Alignment for Point Cloud
• Authors: Mengxi Wu, Hao Huang, Yi Fang, Mohammad Rostami,
• Abstract summary: Unsupervised Domain Adaptation (UDA) is crucial for reducing the need for extensive manual data annotation. We propose textbfCurvature textbfDiversity-Driven textbfNuclear-Norm Wasserstein textbfDomain Alignment (CDND) Our approach first introduces a textittextbfCurvature Diversity-driven Deformation textbfReconstruction (CurvRec) task. We then propose textittextbfD
• Score: 28.713586981346808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised Domain Adaptation (UDA) is crucial for reducing the need for extensive manual data annotation when training deep networks on point cloud data. A significant challenge of UDA lies in effectively bridging the domain gap. To tackle this challenge, we propose \textbf{C}urvature \textbf{D}iversity-Driven \textbf{N}uclear-Norm Wasserstein \textbf{D}omain Alignment (CDND). Our approach first introduces a \textit{\textbf{Curv}ature Diversity-driven Deformation \textbf{Rec}onstruction (CurvRec)} task, which effectively mitigates the gap between the source and target domains by enabling the model to extract salient features from semantically rich regions of a given point cloud. We then propose \textit{\textbf{D}eformation-based \textbf{N}uclear-norm \textbf{W}asserstein \textbf{D}iscrepancy (D-NWD)}, which applies the Nuclear-norm Wasserstein Discrepancy to both \textit{deformed and original} data samples to align the source and target domains. Furthermore, we contribute a theoretical justification for the effectiveness of D-NWD in distribution alignment and demonstrate that it is \textit{generic} enough to be applied to \textbf{any} deformations. To validate our method, we conduct extensive experiments on two public domain adaptation datasets for point cloud classification and segmentation tasks. Empirical experiment results show that our CDND achieves state-of-the-art performance by a noticeable margin over existing approaches.

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