The unsupervised domain adaptation (UDA) method aims to utilize a labeled data domain (source domain) to enhance the model's generalization ability in another unlabeled data domain (target domain). In the three-dimensional (3D) real-world context, significant differences in the geometry and distribution of data exist between the source and target domains. However, current 3D UDA methods have not paid sufficient attention to such domain gap issues, resulting in decreased predictive performance in the target domain. Therefore, a 3D UDA method based on balanced geometric perception is proposed. To achieve consistent underlying geometric information across domains, a self-supervised pretraining task based on implicit fields is designed, which involves training a point cloud implicit field using balanced local distances. Through this method, the model can fully leverage underlying geometric information and effectively learn implicit representations at varying densities, thereby mitigating the impact of outlier point cloud data. In addition, a point cloud hybrid enhancement strategy is adopted to interpolate the point cloud data and labels. This provides more intermediate state information for the point cloud data, increases the diversity of the training data, and further improves the model's generalization ability. Experimental results show that the proposed method achieves a mean intersection-over-union of 65.2% on the segmentation dataset PointSegDA and an accuracy of 71.8% on the classification dataset PointDA-10, demonstrating the effectiveness of the proposed method.