# LassoNet: Deep Lasso-Selection of 3D Point Clouds

## TL;DR

LassoNet is a deep neural network designed to improve the selection of regions in 3D point clouds by learning from user input, viewpoints, and lasso shapes, outperforming existing methods in effectiveness and efficiency.

## Contribution

This work introduces LassoNet, a novel deep learning approach that learns to perform lasso selection on 3D point clouds, addressing variability and scalability challenges.

## Key findings

- LassoNet outperforms state-of-the-art methods in selection accuracy.
- The method improves efficiency in user-guided 3D point cloud selection.
- Evaluation on a large dataset demonstrates robustness across different scenarios.

## Abstract

Selection is a fundamental task in exploratory analysis and visualization of 3D point clouds. Prior researches on selection methods were developed mainly based on heuristics such as local point density, thus limiting their applicability in general data. Specific challenges root in the great variabilities implied by point clouds (e.g., dense vs. sparse), viewpoint (e.g., occluded vs. non-occluded), and lasso (e.g., small vs. large). In this work, we introduce LassoNet, a new deep neural network for lasso selection of 3D point clouds, attempting to learn a latent mapping from viewpoint and lasso to point cloud regions. To achieve this, we couple user-target points with viewpoint and lasso information through 3D coordinate transform and naive selection, and improve the method scalability via an intention filtering and farthest point sampling. A hierarchical network is trained using a dataset with over 30K lasso-selection records on two different point cloud data. We conduct a formal user study to compare LassoNet with two state-of-the-art lasso-selection methods. The evaluations confirm that our approach improves the selection effectiveness and efficiency across different combinations of 3D point clouds, viewpoints, and lasso selections. Project Website: https://lassonet.github.io