NeuralSampler: Euclidean Point Cloud Auto-Encoder and Sampler

Abstract

Most algorithms that rely on deep learning-based approaches to generate 3D point sets can only produce clouds containing fixed number of points. Furthermore, they typically require large networks parameterized by many weights, which makes them hard to train. In this paper, we propose an auto-encoder architecture that can both encode and decode clouds of arbitrary size and demonstrate its effectiveness at upsampling sparse point clouds. Interestingly, we can do so using less than half as many parameters as state-of-the-art architectures while still delivering better performance. We will make our code base fully available.

References in corpus (4)

• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Learning Representations and Generative Models for 3D Point Clouds
• Dynamic Graph CNN for Learning on Point Clouds
• 3D ShapeNets: A Deep Representation for Volumetric Shapes

Cited by in corpus (5)

• Learned Point Cloud Geometry Compression
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey
• Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
• A stepped sampling method for video detection using LSTM
• Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views