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 101–125 of 265 papers

TitleStatusHype
Efficient Converted Spiking Neural Network for 3D and 2D Classification—0
Learnable Skeleton-Aware 3D Point Cloud Sampling—0
CAP: Robust Point Cloud Classification via Semantic and Structural Modeling—0
ViewNet: A Novel Projection-Based Backbone With View Pooling for Few-Shot Point Cloud ClassificationCode1
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
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
Point-Voxel Adaptive Feature Abstraction for Robust Point Cloud ClassificationCode0
Understanding Key Point Cloud Features for Development Three-dimensional Adversarial Attacks—0
Self-Distillation for Unsupervised 3D Domain Adaptation—0
Neural Attentive Circuits—0
Point Transformer V2: Grouped Vector Attention and Partition-based PoolingCode2
APSNet: Attention Based Point Cloud SamplingCode1
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
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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