Abstract: The technique of 3D deformation is one of the hot topics in the field of computer graphics.Current 3D deformation methods mainly learn the changes before and after deformation by aggregating localized adjacent voxel features,and fail to exploit the interrelationship between non-local voxel features,and the absence of contextual information prevents the model from capturing more discriminative features.To address the above problems,this paper designs a voxel deformation network based on environmental information mining,which can extract local and environmental information simultaneously,and extract environmental information from different spatial domains to improve the representation performance of the network,further modeling the relationship before and after the deformation of the object.Firstly,a novel self-attention mechanism is introduced.Specifically,the learning of the non-local dependence of different voxels is proposed to improve the ability of voxel discrimination.Then,a multi-scale analysis method is introduced to extract environmental information in different perceptual fields via multiple dilated convolution with different dilation rates,which provides more informative contextual features for the subsequent models.In addition,this paper analyzes the impact of feature fusion on the model and designs a method based on encoder-decoder feature fusion,which adaptively fuses the features extracted from the encoder and decoder to improve the nonlinear mapping capability of the model.Extensive experiments are conducted on our tooth dataset.The results show that the deformation prediction accuracy of the proposed method is improved compared to existing methods.

Key words: Shape deformation, Voxel, Attention mechanism, Feature fusion, Multiscale analysis