3D Object Classification
42 papers with code • 3 benchmarks • 6 datasets
3D Object Classification is the task of predicting the class of a 3D object point cloud. It is a voxel level prediction where each voxel is classified into a category. The popular benchmark for this task is the ModelNet dataset. The models for this task are usually evaluated with the Classification Accuracy metric.
Image: Sedaghat et al
Benchmarks
These leaderboards are used to track progress in 3D Object Classification
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Datasets
ModelNet
3RScan
Cyclone Data
UR5 Tool Dataset
Most implemented papers
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
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.
OctNet: Learning Deep 3D Representations at High Resolutions
We present OctNet, a representation for deep learning with sparse 3D data.
ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
suryanshkumar/online-joint-depthfusion-and-semantic
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CVPR 2017
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
lorenmt/vsl
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We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Microsoft/O-CNN
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We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis.
General-Purpose Deep Point Cloud Feature Extractor
WDot/G3DNet
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IEEE Winter Conference on Applications of Computer Vision (WACV) 2018
We adopt these graph based methods to 3D point clouds to introduce a generic vector representation of 3D graphs, we call graph 3D (G3D).
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
krips89/mlh_mvcnn
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ECCV 2018
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs.
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.
A Graph-CNN for 3D Point Cloud Classification
maggie0106/Graph-CNN-in-3D-Point-Cloud-Classification
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Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data that is supported on a graph.
Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers
Daniel-Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks
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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.
