3D Object Classification
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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Greatest papers with code
MeshCNN: A Network with an Edge
In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.
3D PART SEGMENTATION 3D SHAPE ANALYSIS CUBE ENGRAVING CLASSIFICATION
OctNet: Learning Deep 3D Representations at High Resolutions
We present OctNet, a representation for deep learning with sparse 3D data.
3D Point Capsule Networks
CVPR 2019
• yongheng1991/3D-point-capsule-networks
• 
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
3D FEATURE MATCHING 3D GEOMETRY PERCEPTION 3D OBJECT CLASSIFICATION 3D OBJECT RECONSTRUCTION 3D PART SEGMENTATION 3D POINT CLOUD MATCHING 3D SHAPE GENERATION 3D SHAPE REPRESENTATION
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
CVPR 2018 • kanezaki/rotationnet
We propose a Convolutional Neural Network (CNN)-based model "RotationNet," which takes multi-view images of an object as input and jointly estimates its pose and object category.
3D OBJECT CLASSIFICATION OBJECT CLASSIFICATION POSE ESTIMATION
Learning a Hierarchical Latent-Variable Model of 3D Shapes
• lorenmt/vsl
• 
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
#3 best model for
3D Object Recognition
on ModelNet40
3D OBJECT CLASSIFICATION 3D OBJECT RECOGNITION 3D RECONSTRUCTION 3D SHAPE GENERATION
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions.
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
• Daniel-Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks
We present a preliminary evaluation of adversarial attacks on deep 3D point cloud classifiers, namely PointNet and PointNet++, by evaluating both white-box and black-box adversarial attacks that were proposed for 2D images and extending those attacks to reduce the perceptibility of the perturbations in 3D space.
3D OBJECT CLASSIFICATION IMAGE CLASSIFICATION OBJECT CLASSIFICATION