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

arXiv: https://arxiv.org/abs/1907.13538 · HTML: https://mlsanity.com/paper/1907.13538/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 10 of 10)

- [Point Cloud Upsampling via Disentangled Refinement](https://mlsanity.com/paper/2106.04779/) (2021)
- [Foundation Models Meet Visualizations: Challenges and Opportunities](https://mlsanity.com/paper/2310.05771/) (2023)
- [PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows](https://mlsanity.com/paper/2107.05893/) (2021)
- [Investigate Indistinguishable Points in Semantic Segmentation of 3D Point Cloud](https://mlsanity.com/paper/2103.10339/) (2021)
- [PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer](https://mlsanity.com/paper/2404.14034/) (2024)
- [Transcending Dimensions using Generative AI: Real-Time 3D Model Generation in Augmented Reality](https://mlsanity.com/paper/2504.21033/) (2025)
- [Local Latent Representation based on Geometric Convolution for Particle Data Feature Exploration](https://mlsanity.com/paper/2105.13240/) (2021)
- [Point Set Self-Embedding](https://mlsanity.com/paper/2202.13577/) (2022)
- [ReViVD: Exploration and Filtering of Trajectories in an Immersive Environment using 3D Shapes](https://mlsanity.com/paper/2202.10545/) (2022)
- [Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling](https://mlsanity.com/paper/2602.10871/) (2026)

## Abstract (arXiv)

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