3D Object Classification

3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.

Image: Sedaghat et al

Papers

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Showing 41–50 of 93 papers

Title	Date	Tasks	Status
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis	Jun 1, 2019	3D Object Classification3D Object Recognition	—Unverified
Continual Learning in 3D Point Clouds: Employing Spectral Techniques for Exemplar Selection	Sep 12, 2024	3D Object ClassificationClustering	—Unverified
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation	Apr 27, 2021	3D Object ClassificationPoint Cloud Segmentation	—Unverified
Data-Free Point Cloud Network for 3D Face Recognition	Nov 12, 2019	3D Object ClassificationFace Recognition	—Unverified
Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems	May 9, 2018	3D Object ClassificationClassification	—Unverified
Dense Graph Convolutional Neural Networks on 3D Meshes for 3D Object Segmentation and Classification	Jun 30, 2021	3D Object ClassificationDescriptive	—Unverified
Efficient Spatio-Temporal Signal Recognition on Edge Devices Using PointLCA-Net	Nov 21, 2024	3D Object ClassificationComputational Efficiency	—Unverified
Fast Sparse 3D Convolution Network with VDB	Nov 5, 2023	3D Object Classification	—Unverified
Formula-Supervised Visual-Geometric Pre-training	Sep 20, 2024	3D Object Classification3D Object Recognition	—Unverified
FusionNet: 3D Object Classification Using Multiple Data Representations	Jul 19, 2016	3D Object Classification3D Object Recognition	—Unverified

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All datasets ModelNet40 ModelNet10 3R-Scan Remote Flash LiDAR Vehicles Dataset

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Ours	Classification Accuracy	93.6	—	Unverified
2	G3DNet-18 MLP, Fine-Tuned, Vote	Classification Accuracy	91.7	—	Unverified
3	CrossMoCo	Classification Accuracy	91.49	—	Unverified
4	O-CNN(6)	Classification Accuracy	89.9	—	Unverified
5	Spherical Kernel	Classification Accuracy	89.3	—	Unverified
6	3D-PointCapsNet	Classification Accuracy	89.3	—	Unverified
7	ECC (12 votes)	Classification Accuracy	83.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PolyNet	Accuracy	94.93	—	Unverified
2	ORION	Accuracy	93.8	—	Unverified
3	G3DNet-18 SVM, Fine-Tuned, Vote	Accuracy	93.1	—	Unverified
4	ECC (12 votes)	Accuracy	90	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SceneGraphFusion	Top-10 Accuracy	0.8	—	Unverified
2	3DSSG [Wald2020_3dssg]	Top-10 Accuracy	0.78	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	YOLO-X	mean average precision	0.99	—	Unverified