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 51–60 of 93 papers

Title	Date	Tasks	Status	Hype
Regularization Strategy for Point Cloud via Rigidly Mixed Sample	Feb 3, 2021	3D Object Classification3D Point Cloud Classification	CodeCode Available	1
Spherical Transformer: Adapting Spherical Signal to CNNs	Jan 11, 2021	3D Object ClassificationGeneral Classification	—Unverified	0
Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis	Dec 25, 2020	3D Object ClassificationSuper-Resolution	—Unverified	0
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud	Dec 20, 2020	3D Object Classification3D Part Segmentation	CodeCode Available	1
Classification of Single-View Object Point Clouds	Dec 18, 2020	3D Object Classification6D Pose Estimation using RGB	—Unverified	0
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting	Dec 16, 2020	3D Object ClassificationFairness	—Unverified	0
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification	Nov 9, 2020	3D Object ClassificationClassification	CodeCode Available	0
Point Transformer	Nov 2, 2020	3D Object Classification3D Part Segmentation	CodeCode Available	1
Cascaded Refinement Network for Point Cloud Completion with Self-supervision	Oct 17, 2020	3D Object ClassificationPoint Cloud Completion	CodeCode Available	1
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks	Aug 1, 2020	3D Action Recognition3D Classification	CodeCode Available	1

Show:10 25 50

← PrevPage 6 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