<p>3D instance segmentation is essential in applications such as autonomous driving, augmented reality, and robotics, where accurate identification of individual objects in complex point cloud data is required. Existing methods typically rely on feature learning in a single spatial domain and often fail in cases involving overlapping objects and sparse point distributions. To solve these problems, we propose 3D-SDIS, a multi-domain 3D instance segmentation network. It includes an Fast Fourier Transform (FFT) Spatial Fusion Encoder (FSF Encoder) that transforms spatial features into the frequency domain. This process reduces interference from redundant points and improves boundary localization. We also introduce an Offset Dual-Sphere Sampling Module (ODSS), which performs multi-view feature sampling based on both the original and offset sphere centers. It increases the receptive field and captures more geometric information. Experimental results on the ScanNetV2 (mAP 62.9) and S3DIS (mAP 61.0) datasets demonstrate the superiority of 3D-SDIS over state-of-the-art methods, especially for handling overlapping instances and large planar structures. The source code and trained models are available at http:// github.com/cbbbbg/3D-SDIS.</p>