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
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
FBPT: A Fully Binary Point Transformer—0
Feature Adversarial Distillation for Point Cloud Classification—0
Few-Data Guided Learning Upon End-to-End Point Cloud Network for 3D Face Recognition—0
Fourier Decomposition for Explicit Representation of 3D Point Cloud Attributes—0
3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds—0
From depth image to semantic scene synthesis through point cloud classification and labeling: Application to assistive systems—0
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs—0
Topologically Persistent Features-based Object Recognition in Cluttered Indoor Environments—0
Global Context Aware Convolutions for 3D Point Cloud Understanding—0
GraNet: Global Relation-aware Attentional Network for ALS Point Cloud Classification—0
GTNet: Graph Transformer Network for 3D Point Cloud Classification and Semantic Segmentation—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
Towards Training Stronger Video Vision Transformers for EPIC-KITCHENS-100 Action Recognition—0
Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey—0
Training or Architecture? How to Incorporate Invariance in Neural Networks—0
Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification—0
Interpreting Hidden Semantics in the Intermediate Layers of 3D Point Cloud Classification Neural Network—0
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks—0
Learnable Skeleton-Aware 3D Point Cloud Sampling—0
Learning Adaptive Neighborhoods for Graph Neural Networks—0
Learning-Based Biharmonic Augmentation for Point Cloud Classification—0
Learning Category-level Shape Saliency via Deep Implicit Surface Networks—0
Transformers in 3D Point Clouds: A Survey—0
Learning Rotation-Invariant Representations of Point Clouds Using Aligned Edge Convolutional Neural Networks—0
Leveraging PointNet and PointNet++ for Lyft Point Cloud Classification Challenge—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