点云是一种重要的三维表达方式,在计算机视觉和机器人领域都有着广泛的应用。由于真实应用场景中存在遮挡和采样不均匀等情况,传感器采集的目标物体点云形状往往是不完整的。为了提取点云的特征和补全目标点云,提出了一种基于多分支结构的点云补全网络。编码器从输入信息中提取局部特征和全局特征,解码器中的多分支结构将提取的特征转换成点云,以得到目标物体完整的点云形状。在ShapeNet和KITTI数据集以及不同残缺比例、不同几何形状的情况下进行实验,结果表明,本方法可以很好地补充目标缺失的点云,得到完整、直观、真实的点云模型。

Point cloud is an important three-dimensional expression, and it has a wide range of applications in computer vision and robotics. Due to occlusion and uneven sampling in real application scenarios, the shape of the target object point cloud collected by the sensor is often incomplete. To achieve the point cloud of feature extraction and shape completion, a new point cloud completion network based on the multibranch structure is proposed in this paper. The encoder is primarily responsible for extracting the global and local features from the input information, and the multibranch structure in the decoder is responsible for converting the features to point clouds to obtain the complete point cloud shape of the object. Experiments are conducted using the ShapeNet and KITTI data sets, with different incomplete proportions and geometric shapes. Results show that the method can well supplement the missing point cloud of the target and obtain a complete, intuitive, and true point cloud model.