SOTAVerified

Point Cloud Classification

Point Cloud Classification is a task involving the classification of unordered 3D point sets (point clouds).

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–250 of 265 papers

TitleStatusHype
PCT: Point cloud transformerCode1
Learning Category-level Shape Saliency via Deep Implicit Surface Networks—0
On Learning the Right Attention Point for Feature Enhancement—0
Sparse Convolutions on Continuous Domains for Point Cloud and Event Stream Networks—0
On Adversarial Robustness of 3D Point Cloud Classification under Adaptive Attacks—0
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud ClassifiersCode1
MGIC: Multigrid-in-Channels Neural Network ArchitecturesCode0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
PointManifold: Using Manifold Learning for Point Cloud Classification—0
Unsupervised Point Cloud Pre-Training via Occlusion CompletionCode1
On The Adversarial Robustness of 3D Point Cloud Classification—0
Multi-scale Receptive Fields Graph Attention Network for Point Cloud ClassificationCode0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation—0
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks—0
PointMixup: Augmentation for Point CloudsCode1
From depth image to semantic scene synthesis through point cloud classification and labeling: Application to assistive systems—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks—0
Point Set Voting for Partial Point Cloud AnalysisCode1
Parameter-Efficient Person Re-identification in the 3D SpaceCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysisCode0
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud DownsamplingCode1
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network—0
Shape-Oriented Convolution Neural Network for Point Cloud Analysis—0
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and ClassificationCode1
Triangle-Net: Towards Robustness in Point Cloud LearningCode0
PointAugment: an Auto-Augmentation Framework for Point Cloud ClassificationCode1
PointHop++: A Lightweight Learning Model on Point Sets for 3D ClassificationCode1
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud ProcessingCode0
Efficient and Stable Graph Scattering Transforms via Pruning—0
Geometry Sharing Network for 3D Point Cloud Classification and SegmentationCode0
Transductive Zero-Shot Learning for 3D Point Cloud ClassificationCode1
Grid-GCN for Fast and Scalable Point Cloud LearningCode0
SGAS: Sequential Greedy Architecture SearchCode0
Geometric Back-projection Network for Point Cloud ClassificationCode0
Learning Permutation Invariant Representations using Memory NetworksCode0
SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation—0
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global TopologyCode0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification—0
Spherical Kernel for Efficient Graph Convolution on 3D Point CloudsCode0
Deep Declarative Networks: A New HopeCode1
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud ClassificationCode0
Adversarial shape perturbations on 3D point cloudsCode0
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World DataCode1
PointHop: An Explainable Machine Learning Method for Point Cloud ClassificationCode1
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant LearningCode0
Structural Relational Reasoning of Point Clouds—0
NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds—0
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural NetworkCode0
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All datasetsPointCloud-CISPRS

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PointNetmean Corruption Error (mCE)1.42—Unverified
2WOLFMix (PointNet)mean Corruption Error (mCE)1.18—Unverified
3PointNetmean Corruption Error (mCE)1.18—Unverified
4RSCNNmean Corruption Error (mCE)1.13—Unverified
5PAConvmean Corruption Error (mCE)1.1—Unverified
6SimpleViewmean Corruption Error (mCE)1.05—Unverified
7OcCo-DGCNNmean Corruption Error (mCE)1.05—Unverified
8PointMixUp (PointNet++)mean Corruption Error (mCE)1.03—Unverified
9DGCNNmean Corruption Error (mCE)1—Unverified
10OcCo-DGCNNmean Corruption Error (mCE)0.98—Unverified
#ModelMetricClaimedVerifiedStatus
1OursAverage F182.8—Unverified