3D Object Classification
42 papers with code • 3 benchmarks • 6 datasets
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
Benchmarks
These leaderboards are used to track progress in 3D Object Classification
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Datasets
ModelNet
3RScan
Cyclone Data
UR5 Tool Dataset
Latest papers
Improved Training for 3D Point Cloud Classification
snehaputul/ImprovedPointCloud
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Structural, Syntactic, and Statistical Pattern Recognition (S+SSPR) 2023
PointNet is a pioneering approach in this direction that feeds the 3D point cloud data directly to a model.
MATE: Masked Autoencoders are Online 3D Test-Time Learners
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
Data Augmentation-free Unsupervised Learning for 3D Point Cloud Understanding
gfmei/softclu
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Unsupervised learning on 3D point clouds has undergone a rapid evolution, especially thanks to data augmentation-based contrastive methods.
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding
mohamedafham/crosspoint
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CVPR 2022
Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
On Automatic Data Augmentation for 3D Point Cloud Classification
Data augmentation is an important technique to reduce overfitting and improve learning performance, but existing works on data augmentation for 3D point cloud data are based on heuristics.
diffConv: Analyzing Irregular Point Clouds with an Irregular View
Standard spatial convolutions assume input data with a regular neighborhood structure.
PointMixer: MLP-Mixer for Point Cloud Understanding
lifebeyondexpectations/eccv22-pointmixer
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MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer.
PolyNet: Polynomial Neural Network for 3D Shape Recognition with PolyShape Representation
myavartanoo/PolyNet_PyTorch
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3D shape representation and its processing have substantial effects on 3D shape recognition.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks.
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
dogyoonlee/RSMix-official
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CVPR 2021
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.
