3D Classification
33 papers with code • 0 benchmarks • 11 datasets
Benchmarks
These leaderboards are used to track progress in 3D Classification
Libraries
Use these libraries to find 3D Classification models and implementationsDatasets
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ShapeNetCore
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ModelNet40-C
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RAD-ChestCT Dataset
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Teeth3DS
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ADHD-200
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Calcium imaging of glomeruli in the olfactory bulb of the mouse in response to thirty-five monomolecular odors
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CVB
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3D-Point Cloud dataset of various geometrical terrains
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Corn Seeds Dataset
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VIDIMU: Multimodal video and IMU kinematic dataset on daily life activities using affordable devices
Latest papers with no code
Deep Models for Multi-View 3D Object Recognition: A Review
We provide detailed information about existing deep learning-based and transformer-based multi-view 3D object recognition models, including the most commonly used 3D datasets, camera configurations and number of views, view selection strategies, pre-trained CNN architectures, fusion strategies, and recognition performance on 3D classification and 3D retrieval tasks.
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
Group Multi-View Transformer for 3D Shape Analysis with Spatial Encoding
The large model GMViT achieves excellent 3D classification and retrieval results on the benchmark datasets ModelNet, ShapeNetCore55, and MCB.
MV-CLIP: Multi-View CLIP for Zero-shot 3D Shape Recognition
Consequently, this paper aims to improve the confidence with view selection and hierarchical prompts.
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey
Deep learning (DL) has become one of the mainstream and effective methods for point cloud analysis tasks such as detection, segmentation and classification.
Improved Cryo-EM Pose Estimation and 3D Classification through Latent-Space Disentanglement
In these methods, only a subset of the input dataset is needed to train neural networks for the estimation of poses and conformations.
Advancing Smart Malnutrition Monitoring: A Multi-Modal Learning Approach for Vital Health Parameter Estimation
Malnutrition poses a significant threat to global health, resulting from an inadequate intake of essential nutrients that adversely impacts vital organs and overall bodily functioning.
DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation.
Self-supervised Learning by View Synthesis
In each iteration, the input to VSA is one view (or multiple views) of a 3D object and the output is a synthesized image in another target pose.
Variational Relational Point Completion Network for Robust 3D Classification
Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details.
