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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 71–80 of 93 papers

Title	Date	Tasks	Status
SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection	Nov 5, 2018	3D Object ClassificationClassification	—Unverified
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity	Feb 22, 2023	3D Object ClassificationObject	—Unverified
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting	Feb 5, 2022	3D Object ClassificationClustering	—Unverified
Unsupervised Contrastive Learning with Simple Transformation for 3D Point Cloud Data	Oct 13, 2021	3D Object ClassificationClassification	—Unverified
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers	Jan 10, 2019	3D Object ClassificationGeneral Classification	CodeCode Available
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs	Apr 10, 2017	3D Object Classification3D Point Cloud Classification	CodeCode Available
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints	Mar 20, 2016	3D Object ClassificationObject	CodeCode Available
OctNet: Learning Deep 3D Representations at High Resolutions	Nov 15, 2016	3D Object ClassificationGeneral Classification	CodeCode Available
Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach	Mar 9, 2024	3D Object ClassificationAutonomous Navigation	CodeCode Available
On Automatic Data Augmentation for 3D Point Cloud Classification	Dec 11, 2021	3D Object Classification3D Object Recognition	CodeCode Available

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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