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 201–225 of 265 papers

TitleStatusHype
Rethinking Attention Module Design for Point Cloud Analysis—0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification—0
DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification—0
Rethinking Gradient-based Adversarial Attacks on Point Cloud Classification—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Dual Transformer for Point Cloud Analysis—0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation—0
A Benchmark Grocery Dataset of Realworld Point Clouds From Single View—0
Edge Aware Learning for 3D Point Cloud—0
Efficient and Stable Graph Scattering Transforms via Pruning—0
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Eidos: Efficient, Imperceptible Adversarial 3D Point Clouds—0
EllipsoidNet: Ellipsoid Representation for Point Cloud Classification and Segmentation—0
Empowering Knowledge Distillation via Open Set Recognition for Robust 3D Point Cloud Classification—0
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding—0
Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions—0
Equivariance with Learned Canonicalization Functions—0
Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits—0
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations—0
Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding—0
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network—0
FA-KPConv: Introducing Euclidean Symmetries to KPConv via Frame Averaging—0
FatNet: A Feature-attentive Network for 3D Point Cloud Processing—0
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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