Change detection (CD) is a technique widely used in remote sensing for identifying the differences between data acquired at different times. Most existing 3D CD approaches voxelize point clouds into 3D grids, project them into 2D images, or rasterize them into digital surface models due to the irregular format of point clouds and the variety of changes in three-dimensional (3D) objects. However, the details of the geometric structure and spatiotemporal sequence information may not be fully utilized. In this article, we propose PBFormer, a transformer network with Siamese architecture, for directly inferring pointwise changes in bi-temporal 3D point clouds. First, we extract point sequences from irregular 3D point clouds using the k-nearest neighbor method. Second, we uniquely use a point transformer network as an encoder to extract point feature information from bitemporal 3D point clouds. Then, we design a module for fusing the spatiotemporal features of bi-temporal point clouds to effectively detect change features. Finally, multilayer perceptrons are used to obtain the CD results. Extensive experiments conducted on the Urb3DCD benchmark show that PBFormer outperforms other excellent approaches for 3D point cloud CD tasks.