Point Cloud Classification
111 papers with code • 2 benchmarks • 2 datasets
Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).
Libraries
Use these libraries to find Point Cloud Classification models and implementationsMost implemented papers
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
A number of problems can be formulated as prediction on graph-structured data.
Adversarial shape perturbations on 3D point clouds
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.
Geometric Back-projection Network for Point Cloud Classification
As the basic task of point cloud analysis, classification is fundamental but always challenging.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
Revisiting Point Cloud Classification with a Simple and Effective Baseline
It also outperforms state-of-the-art methods on ScanObjectNN, a real-world point cloud benchmark, and demonstrates better cross-dataset generalization.
PointCutMix: Regularization Strategy for Point Cloud Classification
As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent.
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point Clouds
The key of PAConv is to construct the convolution kernel by dynamically assembling basic weight matrices stored in Weight Bank, where the coefficients of these weight matrices are self-adaptively learned from point positions through ScoreNet.
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
Discrete point cloud objects lack sufficient shape descriptors of 3D geometries.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.