3D Classification
42 papers with code • 1 benchmarks • 15 datasets
Benchmarks
These leaderboards are used to track progress in 3D Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Libraries
Use these libraries to find 3D Classification models and implementationsDatasets
ShapeNetCore
ModelNet40-C
RAD-ChestCT Dataset
Teeth3DS+
CVB
3D-Point Cloud dataset of various geometrical terrains
U-10: United-10 COVID19 CT Dataset
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.
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.
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.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
yangyangyang127/pointclip_v2
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ICCV 2023
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
Robustifying Point Cloud Networks by Refocusing
In this study, we develop a general mechanism to increase neural network robustness based on focus analysis.
Virtual imaging trials improved the transparency and reliability of AI systems in COVID-19 imaging
The study underscores the potential of VIT to improve the reproducibility and clinical relevance of AI systems in medical imaging.
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.
