Compared with 2D RGB-based images， 3D point clouds retain the real and rich geometric information of objects in space to deal with vision challenge with scale variation in the single object tracking problem. However， the precision of 3D object tracking is affected by the loss of information brought by the sparsity of point cloud data and the deformation caused by the object position changing. To solve the above two problems， a proposal-based aggregation network composed of three modules was proposed in an end-to-end learning pattern. In this network， the 3D bounding box was determined by locating object center in the best proposal to realize the single object tracking in 3D point cloud. Firstly， the point cloud data of both templates and search areas was transferred into bird’s-eye view pseudo images. In the first module， the feature information was enriched through spatial and cross-channel attention mechanisms. Then， in the second module， the best proposal was given by the anchor-based deep cross-correlation Siamese region proposal subnetwork. Finally， in the third module， the object features were extracted through region of interest pooling operation by the best proposal at first， and then， the object and template features were aggregated， the sparse modulated deformable convolution layer was used to deal with the problems of point cloud sparsity and deformation， and the final 3D bounding box was determined. Experimental results of the comparison between the proposed method and the state-of-the-art 3D point cloud single object tracking methods on KITTI dataset show that： in comprehensive experiment of car， the proposed method has improved 1.7 percentage points on success rate and 0.2 percentage points on precision in real scenes； in multi-category extensive experiment of car， van， cyclist and pedestrian， the proposed method has improved the average success rate by 0.8 percentage points， and the average precision by 2.8 percentage points， indicating that the proposed method can solve the single object tracking problem in 3D point cloud and make the 3D object tracking results more accurate.

Concerning the special requirements of collision detection in the virtual assembly environment, a virtual assembly-oriented two-layer exact collision detection algorithm named HSDHBB was proposed based on bounding volume boxes and space division method. The algorithm firstly usd space decomposition method to identify potential regional intersection and then used bounding volume boxes to locate the intersection triangles and the exact points. Methods of constructing the bounding volume boxes tree and space division were given, and the data structure of Hash table was used to accelerate the collision detection in space division. Finally, the algorithm was applied in CATIA, the results show that the algorithm can effectively meet the real-time and accuracy requirements of the virtual assembly environment.