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 31–40 of 93 papers

Title	Date	Tasks	Status	Hype
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding	Nov 22, 2022	3D Object ClassificationRepresentation Learning	—Unverified	0
MATE: Masked Autoencoders are Online 3D Test-Time Learners	Nov 21, 2022	3D Object ClassificationPoint Cloud Classification	CodeCode Available	1
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding	Oct 6, 2022	3D Object ClassificationContrastive Learning	CodeCode Available	1
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives	May 25, 2022	3D Object ClassificationDataset Distillation	—Unverified	0
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding	Mar 1, 2022	3D Object Classification3D Point Cloud Linear Classification	CodeCode Available	2
Unsupervised Learning on 3D Point Clouds by Clustering and Contrasting	Feb 5, 2022	3D Object ClassificationClustering	—Unverified	0
On Automatic Data Augmentation for 3D Point Cloud Classification	Dec 11, 2021	3D Object Classification3D Object Recognition	CodeCode Available	0
diffConv: Analyzing Irregular Point Clouds with an Irregular View	Nov 29, 2021	3D Object Classification3D Point Cloud Classification	CodeCode Available	1
PointMixer: MLP-Mixer for Point Cloud Understanding	Nov 22, 2021	3D Object Classification3D Point Cloud Classification	CodeCode Available	1
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation	Nov 14, 2021	3D Object ClassificationPoint Cloud Segmentation	—Unverified	0

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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