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
Latest papers
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).
Cross-Domain 3D Equivariant Image Embeddings
machc/spherical_embeddings
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This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
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.
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.
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
code-implementation1/Code9
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With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
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.
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.
Beam Search for Learning a Deep Convolutional Neural Network of 3D Shapes
Each state of the beam search corresponds to a candidate CNN.
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.
