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 21–30 of 93 papers

Title	Date	Tasks	Status	Hype
PointLLM: Empowering Large Language Models to Understand Point Clouds	Aug 31, 2023	3D Object Captioning3D Object Classification	CodeCode Available	2
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	0
CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud	Jun 8, 2023	3D Object Classification3D Point Cloud Classification	CodeCode Available	0
Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation	Apr 27, 2023	3D Object Classification3D Part Segmentation	CodeCode Available	1
MVImgNet: A Large-scale Dataset of Multi-view Images	Mar 10, 2023	3D Object ClassificationAttribute	—Unverified	0
Densely Connected G-invariant Deep Neural Networks with Signed Permutation Representations	Mar 8, 2023	3D Object Classification	CodeCode Available	0
Unsupervised 3D Object Learning through Neuron Activity aware Plasticity	Feb 22, 2023	3D Object ClassificationObject	—Unverified	0
PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration	Feb 6, 2023	3D Object ClassificationDisentanglement	CodeCode Available	0
Improved Training for 3D Point Cloud Classification	Jan 1, 2023	3D Object Classification3D Point Cloud Classification	CodeCode Available	0
HGNet: Learning Hierarchical Geometry From Points, Edges, and Surfaces	Jan 1, 2023	3D Object ClassificationAll	—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