Point Cloud Classification
58 papers with code • 1 benchmarks • 1 datasets
Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
Attention mechanisms, especially self-attention, have played an increasingly important role in deep feature representation for visual tasks.
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
In the attribute building stage, we address the problem of unordered point cloud data using a space partitioning procedure and developing a robust descriptor that characterizes the relationship between a point and its one-hop neighbor in a PointHop unit.
The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving.
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.