LassoNet

LassoNet applies a hierarchical deep neural network to perform lasso selection on 3D point clouds by mapping viewpoints and lasso shapes to target point-cloud regions for accurate region selection.

Key Features:

• Deep Neural Network Architecture: Hierarchical deep neural network maps relationships between viewpoints, lasso shapes, and point-cloud regions to handle variable densities, occlusions, and lasso sizes.
• Latent Mapping Learning: Learns a latent mapping from viewpoint and lasso configurations to specific regions in point clouds to improve selection accuracy over heuristic density-based methods.
• Data-Driven Approach: Trained on over 30,000 lasso-selection records derived from two distinct point cloud datasets.
• Scalability Enhancements: Incorporates intention filtering and farthest point sampling to manage large-scale point clouds while maintaining selection accuracy.
• User-Target Coupling: Integrates user-target points with viewpoint and lasso information using 3D coordinate transformation and naive selection processes to align selections with user intent.

Scientific Applications:

• Computer Graphics: Supports selection and manipulation of regions in 3D models and point-cloud-based scenes.
• Virtual Reality: Enables precise region selection in immersive 3D environments and VR workflows.
• Geographic Information Systems (GIS): Facilitates selection and analysis of spatial regions within large geospatial point-cloud datasets.
• Bioinformatics: Assists analyses that employ 3D point-cloud representations within bioinformatics workflows.

Methodology:

Train a hierarchical network on a dataset of over 30,000 lasso-selection records from two point-cloud datasets to predict point-cloud regions from input viewpoints and lasso shapes, incorporating intention filtering, farthest point sampling, 3D coordinate transformation, and naive selection processes.