SOTAVerified

Point Cloud Classification

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

Papers

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Showing 101–150 of 265 papers

TitleStatusHype
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud ClassificationCode1
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
Learnable Skeleton-Aware 3D Point Cloud Sampling—0
Implicit Convolutional Kernels for Steerable CNNsCode1
Local Neighborhood Features for 3D ClassificationCode0
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud AnalysisCode1
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples—0
MATE: Masked Autoencoders are Online 3D Test-Time LearnersCode1
Equivariance with Learned Canonicalization Functions—0
Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud ClassificationCode0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks—0
Self-Distillation for Unsupervised 3D Domain Adaptation—0
Neural Attentive Circuits—0
APSNet: Attention Based Point Cloud SamplingCode1
Point Transformer V2: Grouped Vector Attention and Partition-based PoolingCode2
CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingCode1
Automated Mobile Attention KPConv Networks via a Wide and Deep Predictor—0
A Simple Strategy to Provable Invariance via Orbit Mapping—0
3DGTN: 3D Dual-Attention GLocal Transformer Network for Point Cloud Classification and Segmentation—0
Rethinking the compositionality of point clouds through regularization in the hyperbolic spaceCode1
SimpleView++: Neighborhood Views for Point Cloud ClassificationCode0
Scalable Set Encoding with Universal Mini-Batch Consistency and Unbiased Full Set Gradient ApproximationCode1
Pix4Point: Image Pretrained Standard Transformers for 3D Point Cloud UnderstandingCode1
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification—0
Boosting Point-BERT by Multi-choice TokensCode0
Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations—0
Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classificationCode1
PointNorm: Dual Normalization is All You Need for Point Cloud AnalysisCode1
Enhancing Local Feature Learning Using Diffusion for 3D Point Cloud Understanding—0
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces—0
AGConv: Adaptive Graph Convolution on 3D Point CloudsCode0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments—0
Transformers in 3D Point Clouds: A Survey—0
APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud ClassificationCode1
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding—0
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit GradientsCode1
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time SurfacesCode1
Visualizing Global Explanations of Point Cloud DNNsCode1
Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain GapCode1
DuMLP-Pin: A Dual-MLP-dot-product Permutation-invariant Network for Set Feature ExtractionCode0
3DCTN: 3D Convolution-Transformer Network for Point Cloud ClassificationCode1
LPF-Defense: 3D Adversarial Defense based on Frequency AnalysisCode1
Benchmarking and Analyzing Point Cloud Classification under CorruptionsCode1
Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud ClassificationCode0
Action Keypoint Network for Efficient Video Recognition—0
Why Discard if You Can Recycle?: A Recycling Max Pooling Module for 3D Point Cloud AnalysisCode1
Learnable Lookup Table for Neural Network QuantizationCode1
On Automatic Data Augmentation for 3D Point Cloud ClassificationCode0
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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