3D object detection based on point cloud is a challenging visual task, which is helpful to the realization of various 3D visual applications. A few recent works based votenet recognize objects by using hough voting. However, the voting strategy in votenet can only obtain some sampling points from incomplete surfaces and chaotic backgrounds, without considering the features and position relation of the original cloud points. In this work, we introduce a new 3D object detection method called feature fusion based revoting network (FFRNet). Our method adds a supervision mechanism to the simple voting mechanism and fuses the feature of seed points and voting points to increase the richness of information in the re-voting module. The feature fusion operation enhances the acquisition of effective information of the original surface points, So as to achieve more reliable and flexible object positioning and category prediction results. We validate our model on the challenging ScanNet V2 dataset, advancing votenet results by 3.6 [email protected].