Abstract Currently, 2D anomaly detection has demonstrated outstanding performance. However, 2D images limit the improvement of anomaly detection accuracy without utilizing depth information. Therefore, this paper proposes a Dual Reconstruction viAInpainting Network for 3D industrial anomaly detection (DRAIN). Firstly, we design a 3D reconstruction network using an encoder-decoder-based U-shaped network for processing RGB images and depth images. Subsequently, accurate anomaly segmentation is implemented through a 3D segmentation network. We introduce a lightweight MLP module to enhance segmentation performance to capture long-range dependencies in the reconstructed images. Furthermore, we propose a dual attention-based information entropy fusion module to expedite feature fusion in the inference process, aiming for enhanced deployment in the industry. Extensive experiments demonstrate that DRAIN achieves a 94.3% AUROC on the 3D anomaly detection dataset MVTec 3D-AD, surpassing other research methods. Graphical abstract Overall architecture for 3D industrial anomaly detection via dual reconstruction network