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
Most implemented papers
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.
Convolution in the Cloud: Learning Deformable Kernels in 3D Graph Convolution Networks for Point Cloud Analysis
j1a0m0e4sNTU/3dgcn
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CVPR 2020
Point clouds are among the popular geometry representations for 3D vision applications.
MeshWalker: Deep Mesh Understanding by Random Walks
Each walk is organized as a list of vertices, which in some manner imposes regularity on the mesh.
Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features
Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance.
Task-Oriented Feature Distillation
ArchipLab-LinfengZhang/Task-Oriented-Feature-Distillation
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NeurIPS 2020
Moreover, an orthogonal loss is applied to the feature resizing layer in TOFD to improve the performance of knowledge distillation.
Deep manifold learning reveals hidden dynamics of proteasome autoregulation
The 2. 5-MDa 26S proteasome maintains proteostasis and regulates myriad cellular processes.
Convolutional neural networks for Alzheimer’s disease detection on MRI images
Conclusions: Several implementations and experiments using CNNs on MRI scans for AD detection demonstrated that the voxel-based method with transfer learning from ImageNet to MRI datasets using 3D CNNs considerably improved the results compared with the others.
Subdivision-Based Mesh Convolution Networks
Meshes with arbitrary connectivity can be remeshed to have Loop subdivision sequence connectivity via self-parameterization, making SubdivNet a general approach.
Learning Inner-Group Relations on Point Clouds
We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation.
Dynamic Local Geometry Capture in 3D PointCloud Classification
VimsLab/DynamicScale
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IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR) 2021
We propose a novel technique of dynamically oriented and scaled ellipsoid based on unique local information to capture the local geometry better.
