3DContextNet: K-d Tree Guided Hierarchical Learning of Point Clouds Using Local and Global Contextual Cues
This work provides a more effective method for 3D scene understanding, which is beneficial for applications like robotics and autonomous navigation.
This paper addresses the challenge of classifying and segmenting 3D point clouds by proposing a method that leverages both local and global contextual cues using a k-d tree structure. The model learns representation vectors progressively along the tree, achieving state-of-the-art performance on 3D scene semantic segmentation on the S3DIS dataset.
Classification and segmentation of 3D point clouds are important tasks in computer vision. Because of the irregular nature of point clouds, most of the existing methods convert point clouds into regular 3D voxel grids before they are used as input for ConvNets. Unfortunately, voxel representations are highly insensitive to the geometrical nature of 3D data. More recent methods encode point clouds to higher dimensional features to cover the global 3D space. However, these models are not able to sufficiently capture the local structures of point clouds. Therefore, in this paper, we propose a method that exploits both local and global contextual cues imposed by the k-d tree. The method is designed to learn representation vectors progressively along the tree structure. Experiments on challenging benchmarks show that the proposed model provides discriminative point set features. For the task of 3D scene semantic segmentation, our method significantly outperforms the state-of-the-art on the Stanford Large-Scale 3D Indoor Spaces Dataset (S3DIS).