Abstract: The computational cost of the voxel-based 3D object reconstruction grows cubically with the in-crease of the resolution.To address this problem,the proposed component-aware 3D object reconstruction method decomposes a 3D object into several components to reconstruct the 3D object by predicting and as-sembling a series of components,which transforms the high-resolution 3D object reconstruction into a series of low-resolution component reconstruction.The proposed method predicts the positions of all components using a component position prediction module.Then the geometric and appearance feature of a component are fused into a joint feature with a component feature extraction module.The joint feature is utilized by a component shape reconstruction module to predict the geometry of components.Finally,all components are assembled into a high-resolution 3D object with the guidance of their positions.Experiments are performed on ShapeNet dataset using an NVIDIA 1080 Maxwell GPU with 12GB of memory.The comparison methods include an octree-based high-resolution reconstruction method,a LSTM-based low-resolution reconstruction method and a baseline method using encoder-decoder architecture.The results of high-resolution recon-struction experiment demonstrate that the component-aware 3D reconstruction method achieves a satisfac-tory 3D reconstruction accuracy with a low computational cost.In the low-resolution reconstruction experiment,the proposed method also performs better and the average accuracy in 13 categories reaches 0.618.