To identify and localize macromolecules of interest in crowded intracellular environment, the low signal-to-noise ratio and missing imaging wedge of cryo-electron tomography (cryo-ET) data pose substantial technical challenges. Currently, mainstream approaches of 3D particle picking in cryo-ET either follow the ‘segment-then-cluster’ strategy, or extract potential structural regions as sub-tomograms and then perform classification. Different from these two-step methods, we solve the problem using a one-step instance segmentation approach, termed 3D-SOLOv2. Specifically, the category and mask of each 3D particle are predicted according to the particle’s location and size. To solve the lack of real masks for 3D particles in cryo-ET, a Gaussian-shaped mask is proposed to approximate real masks. When tested on simulated datasets of SHREC2020 challenge, our model achieves the fastest inference speed and the state-of- the-art performance for both localization and classification tasks. When tested on real cryo-ET dataset of EMPIAR-10045, our model also achieves better performance than other methods.