3D Shape Classification
29 papers with code • 1 benchmarks • 1 datasets
Image: Sun et al
Benchmarks
These leaderboards are used to track progress in 3D Shape Classification
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Libraries
Use these libraries to find 3D Shape Classification models and implementations
Pix3D
Most implemented papers
Deep Learning for 3D Point Clouds: A Survey
To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.
Generating 3D Adversarial Point Clouds
xiangchong1/3d-adv-pc
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CVPR 2019
Deep neural networks are known to be vulnerable to adversarial examples which are carefully crafted instances to cause the models to make wrong predictions.
MeshNet: Mesh Neural Network for 3D Shape Representation
iMoonLab/MeshNet
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However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data.
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
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.
Learning Equivariant Representations
daniilidis-group/spherical-cnn
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In this thesis, we extend equivariance to other kinds of transformations, such as rotation and scaling.
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval
nenadmarkus/wlrn
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Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs.
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Each state of the beam search corresponds to a candidate CNN.
Triplet-Center Loss for Multi-View 3D Object Retrieval
popcornell/keras-triplet-center-loss
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CVPR 2018
Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learning models with softmax loss for the classification of 3D data, while learning discriminative features with deep metric learning for 3D object retrieval is more or less neglected.
Deep Learning for Hand Gesture Recognition on Skeletal Data
guillaumephd/deep_learning_hand_gesture_recognition
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IEEE FG 2018 2018
In this paper, we introduce a new 3D hand gesture recognition approach based on a deep learning model.
