Voxel is an important format to represent geometric data, which has been
widely used for 3D deep learning in shape analysis due to its generalization
ability and regular data format. However, fine-grained tasks like part
segmentation require detailed structural information, which increases voxel
resolution and thus causes other issues such as the exhaustion of computational
resources. In this paper, we propose a novel volumetric convolutional neural
network, which could extract discriminative features encoding detailed
information from voxelized 3D data under a limited resolution. To this purpose,
a spatial dense extraction (SDE) module is designed to preserve the spatial
resolution during the feature extraction procedure, alleviating the loss of
detail caused by sub-sampling operations such as max-pooling. An attention
feature aggregation (AFA) module is also introduced to adaptively select
informative features from different abstraction scales, leading to segmentation
with both semantic consistency and high accuracy of details. Experiment results
on the large-scale dataset demonstrate the effectiveness of our method in 3D
shape part segmentation.

本文提出了一种新颖的体积卷积神经网络，通过空间密集提取模块和注意力特征聚合模块从体素化三维数据中提取具有详细信息的判别特征，以实现具有语义一致性和高精度细节的分割任务，实验结果表明了在大规模数据集上进行形状部分分割任务时我们方法的有效性。