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Solving the 3D Point Cloud Place Recognition (3D-PCPR) task is essential for the localization and mapping of depth-based perception systems. Visual Place Recognition methods are highly dependent on image texture information, while the limited number of available point cloud datasets for 3D-PCPR causes the methods to overfit to specific data. Our objective is to use the latest foundation models for 3D point clouds. These models are trained using enormous 3D object datasets where the density is nearly uniform. However, the point clouds produced by the LiDAR sensors are sparse and non-uniformly distributed. We propose a new approach, Unified Point Cloud 3D Place Recognition (Uni-PCPR), effectively maintaining the expressiveness of features generated by the foundation model. We have evaluated the performance of Uni-PCPR on several datasets and found that it generalizes well to unseen data, outperforming other methods. The code will be available upon acceptance.