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However, standard MLPs are limited in their ability to extract local features effectively. To address this limitation, we propose a Vector-oriented Point Set Abstraction that can aggregate neighboring features through higher-dimensional vectors. To facilitate network optimization, we construct a transformation from scalar to vector using independent angles based on 3D vector rotations. Finally, we develop a PointVector model that follows the structure of PointNeXt. Our experimental results demonstrate that PointVector achieves state-of-the-art performance $\\textbf{72.3\\% mIOU}$ on the S3DIS Area 5 and $\\textbf{78.4\\% mIOU}$ on the S3DIS (6-fold cross-validation) with only $\\textbf{58\\%}$ model parameters of PointNeXt. We hope our work will help the exploration of concise and effective feature representations. 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