3D Object Recognition
27 papers with code • 4 benchmarks • 8 datasets
3D object recognition is the task of recognising objects from 3D data.
Note that there are related tasks you can look at, such as 3D Object Detection which have more leaderboards.
(Image credit: Look Further to Recognize Better)
Benchmarks
These leaderboards are used to track progress in 3D Object Recognition
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Datasets
ModelNet
SHREC
Washington RGB-D
Cube++
DOORS
CY101 Dataset
Corn Seeds Dataset
Latest papers
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.
Learning and Visualizing Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes
3D Convolutional Neural Networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object.
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.
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
zck119/3dgan-release
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NeurIPS 2016
We study the problem of 3D object generation.
FPNN: Field Probing Neural Networks for 3D Data
Each field probing filter is a set of probing points --- sensors that perceive the space.
Volumetric and Multi-View CNNs for Object Classification on 3D Data
charlesq34/3dcnn.torch
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CVPR 2016
Empirical results from these two types of CNNs exhibit a large gap, indicating that existing volumetric CNN architectures and approaches are unable to fully exploit the power of 3D representations.
Sparse 3D convolutional neural networks
facebookresearch/SparseConvNet
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We have implemented a convolutional neural network designed for processing sparse three-dimensional input data.
