Point cloud data, representing the precise 3D layout of the scene, quickly drives the research of 3D object detection. However, the challenge arises due to the rapid iteration of 3D sensors, which leads to significantly different distributions in point clouds. This, in turn, results in subpar performance of 3D cross-sensor object detection. This paper introduces a Cross Mechanism Dataset, named CMD, to support research tackling this challenge. CMD is the first domain adaptation dataset, comprehensively encompassing diverse mechanical sensors and various scenes for 3D object detection. In terms of sensors, CMD includes 32-beam LiDAR, 128-beam LiDAR, solid-state LiDAR, 4D millimeter-wave radar, and cameras, all of which are well-synchronized and calibrated. Regarding the scenes, CMD consists of 50 sequences collocated from different scenarios, ranging from campuses to highways. Furthermore, we validated the effectiveness of various domain adaptation methods in mitigating sensor-based domain differences. We also proposed a DIG method to reduce domain disparities from the perspectives of Density, Intensity, and Geometry, which effectively bridges the domain gap between different sensors. The experimental results on the CMD dataset show that our proposed DIG method outperforms the state-of-the-art techniques, demonstrating the effectiveness of our baseline method. The dataset and the corresponding code are available at https://github.com/im-djh/CMD.