3D Classification
29 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in 3D Classification
Libraries
Use these libraries to find 3D Classification models and implementations
ShapeNetCore
ModelNet40-C
ADHD-200
Corn Seeds Dataset
InfiniteRep
Most implemented papers
Learning SO(3) Equivariant Representations with Spherical CNNs
daniilidis-group/spherical-cnn
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ECCV 2018
We address the problem of 3D rotation equivariance in convolutional neural networks.
PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.
MVTN: Multi-View Transformation Network for 3D Shape Recognition
MVTN exhibits clear performance gains in the tasks of 3D shape classification and 3D shape retrieval without the need for extra training supervision.
Deep Learning Based Automated COVID-19 Classification from Computed Tomography Images
IDU-CVLab/COV19D
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Secondly, the original dataset was processed via anatomy-relevant masking of slice, removing none-representative slices from the CT volume, and hyperparameters tuning.
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding
To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points.
PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies
In this work, we revisit the classical PointNet++ through a systematic study of model training and scaling strategies, and offer two major contributions.
PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning
yangyangyang127/pointclip_v2
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Contrastive Language-Image Pre-training (CLIP) has shown promising open-world performance on 2D image tasks, while its transferred capacity on 3D point clouds, i. e., PointCLIP, is still far from satisfactory.
3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models
dkoguciuk/ensemble_learning_for_point_clouds
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In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection.
Enhancing Learnability of classification algorithms using simple data preprocessing in fMRI scans of Alzheimer's disease
Alzheimer's Disease (AD) is the most common type of dementia.
Triangle-Net: Towards Robustness in Point Cloud Learning
Previous research has shown that points' sparsity, rotation and positional inherent variance can lead to a significant drop in the performance of point cloud based classification techniques.
