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Found 7 papers, 4 papers with code
Continual learning on 3D point clouds with random compressed rehearsal
By using rehearsal and reconstruction as regularization methods of the learning process, our approach achieves a significant decrease of catastrophic forgetting compared to the existing solutions on several most popular point cloud datasets considering two continual learning settings: when a task is known beforehand, and in the challenging scenario of when task information is unknown to the model.
Representing Point Clouds with Generative Conditional Invertible Flow Networks
To exploit similarities between same-class objects and to improve model performance, we turn to weight sharing: networks that model densities of points belonging to objects in the same family share all parameters with the exception of a small, object-specific embedding vector.
Hypernetwork approach to generating point clouds
The main idea of our HyperCloud method is to build a hyper network that returns weights of a particular neural network (target network) trained to map points from a uniform unit ball distribution into a 3D shape.
Conditional Invertible Flow for Point Cloud Generation
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models.
Generative Adversarial Networks: recent developments
In traditional generative modeling, good data representation is very often a base for a good machine learning model.
Semi-supervised learning with Bidirectional GANs
In this work we introduce a novel approach to train Bidirectional Generative Adversarial Model (BiGAN) in a semi-supervised manner.
Adversarial Autoencoders for Compact Representations of 3D Point Clouds
Deep generative architectures provide a way to model not only images but also complex, 3-dimensional objects, such as point clouds.
