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
ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis	Jun 1, 2019	3D Object Classification3D Object Recognition	—Unverified
Spherical Fractal Convolutional Neural Networks for Point Cloud Recognition	Jun 1, 2019	3D Object Classification3D Part Segmentation	—Unverified
Octree guided CNN with Spherical Kernels for 3D Point Clouds	Feb 28, 2019	3D Object Classification3D Part Segmentation	—Unverified
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers	Jan 10, 2019	3D Object ClassificationGeneral Classification	CodeCode Available
3D Point Capsule Networks	Dec 27, 2018	3D Feature Matching3D Geometry Perception	CodeCode Available
3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN	Dec 15, 2018	3D geometry3D Object Classification	—Unverified
A Graph-CNN for 3D Point Cloud Classification	Nov 28, 2018	3D Object Classification3D Point Cloud Classification	CodeCode Available
SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection	Nov 5, 2018	3D Object ClassificationClassification	—Unverified
MeshCNN: A Network with an Edge	Sep 16, 2018	3D Object Classification3D Part Segmentation	CodeCode Available
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks	Jul 23, 2018	3D Object ClassificationGeneral Classification	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