3D Classification
34 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
Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps
We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice 3-chamber cine MRI from N = 576 patients.
Comparing 3D deformations between longitudinal daily CBCT acquisitions using CNN for head and neck radiotherapy toxicity prediction
Accuracies of 85. 8% and 75. 3% was found for radionecrosis and hospitalization, respectively, with similar performance as early as after the first week of treatment.
Classification of FIB/SEM-tomography images for highly porous multiphase materials using random forest classifiers
FIB/SEM tomography represents an indispensable tool for the characterization of three-dimensional nanostructures in battery research and many other fields.
SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems
We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints.
Localized Perturbations For Weakly-Supervised Segmentation of Glioma Brain Tumours
Deep convolutional neural networks (CNNs) have become an essential tool in the medical imaging-based computer-aided diagnostic pipeline.
VA-GCN: A Vector Attention Graph Convolution Network for learning on Point Clouds
Owing to the development of research on local aggregation operators, dramatic breakthrough has been made in point cloud analysis models.
Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks
As a second contribution, we propose to improve the graph construction for GNNs for 3D point clouds.
Concentric Spherical GNN for 3D Representation Learning
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.
The Card Shuffling Hypotheses: Building a Time and Memory Efficient Graph Convolutional Network
State-of-the-art GCNs adopt $K$-nearest neighbor (KNN) searches for local feature aggregation and feature extraction operations from layer to layer.
Cross-Modality 3D Object Detection
In this paper, we focus on exploring the fusion of images and point clouds for 3D object detection in view of the complementary nature of the two modalities, i. e., images possess more semantic information while point clouds specialize in distance sensing.
