SOTAVerified

Point Cloud Classification

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

Papers

Showing 226250 of 265 papers

TitleStatusHype
Multi-scale Receptive Fields Graph Attention Network for Point Cloud ClassificationCode0
Unsupervised Feedforward Feature (UFF) Learning for Point Cloud Classification and Segmentation0
LC-NAS: Latency Constrained Neural Architecture Search for Point Cloud Networks0
From depth image to semantic scene synthesis through point cloud classification and labeling: Application to assistive systems0
Global Context Aware Convolutions for 3D Point Cloud Understanding0
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks0
Point-PlaneNet: Plane kernel based convolutional neural network for point clouds analysisCode0
Shape-Oriented Convolution Neural Network for Point Cloud Analysis0
Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network0
Triangle-Net: Towards Robustness in Point Cloud LearningCode0
AdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud ProcessingCode0
Efficient and Stable Graph Scattering Transforms via Pruning0
Geometry Sharing Network for 3D Point Cloud Classification and SegmentationCode0
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 Segmentation0
Rotation Invariant Point Cloud Classification: Where Local Geometry Meets Global TopologyCode0
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification0
Spherical Kernel for Efficient Graph Convolution on 3D Point CloudsCode0
Directionally Constrained Fully Convolutional Neural Network For Airborne Lidar Point Cloud ClassificationCode0
Adversarial shape perturbations on 3D point cloudsCode0
ACNe: Attentive Context Normalization for Robust Permutation-Equivariant LearningCode0
Structural Relational Reasoning of Point Clouds0
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Benchmark Results

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