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 41–50 of 93 papers

Title	Date	Tasks	Status
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease	Aug 15, 2023	3D Object ClassificationInterpretable Machine Learning	—Unverified
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud	Jun 8, 2023	3D Object Classification3D Point Cloud Classification	CodeCode Available
MVImgNet: A Large-scale Dataset of Multi-view Images	Mar 10, 2023	3D Object ClassificationAttribute	—Unverified
Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations	Mar 8, 2023	3D Object Classification	CodeCode Available
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity	Feb 22, 2023	3D Object ClassificationObject	—Unverified
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration	Feb 6, 2023	3D Object ClassificationDisentanglement	CodeCode Available
Improved Training for 3D Point Cloud Classification	Jan 1, 2023	3D Object Classification3D Point Cloud Classification	CodeCode Available
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces	Jan 1, 2023	3D Object ClassificationAll	—Unverified
PointCMC: Cross-Modal Multi-Scale Correspondences Learning for Point Cloud Understanding	Nov 22, 2022	3D Object ClassificationRepresentation Learning	—Unverified
Primitive3D: 3D Object Dataset Synthesis from Randomly Assembled Primitives	May 25, 2022	3D Object ClassificationDataset Distillation	—Unverified

Show:10 25 50

← PrevPage 5 of 10Next →

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