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 61–70 of 93 papers

Title	Date	Tasks	Status	Hype
Generalized Multi-view Shared Subspace Learning using View Bootstrapping	May 12, 2020	3D Object ClassificationFace Recognition	—Unverified	0
Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds	Mar 29, 2020	3D Object ClassificationGeneral Classification	CodeCode Available	1
FPConv: Learning Local Flattening for Point Convolution	Feb 25, 2020	3D Object ClassificationScene Segmentation	CodeCode Available	1
InSphereNet: a Concise Representation and Classification Method for 3D Object	Dec 25, 2019	3D Object ClassificationClassification	CodeCode Available	0
L3DOC: Lifelong 3D Object Classification	Dec 12, 2019	3D geometry3D Object Classification	—Unverified	0
Data-Free Point Cloud Network for 3D Face Recognition	Nov 12, 2019	3D Object ClassificationFace Recognition	—Unverified	0
Multi-Task, Multi-Channel, Multi-Input Learning for Mental Illness Detection using Social Media Text	Nov 1, 2019	3D Object ClassificationMulti-class Classification	—Unverified	0
Addressing the Sim2Real Gap in Robotic 3D Object Classification	Oct 28, 2019	3D Object ClassificationClassification	—Unverified	0
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds	Sep 20, 2019	3D Instance Segmentation3D Object Classification	CodeCode Available	0
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data	Aug 13, 2019	3D Object Classification3D Point Cloud Classification	CodeCode Available	1

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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