# LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images

arXiv: https://arxiv.org/abs/1910.13955 · HTML: https://mlsanity.com/paper/1910.13955/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 7 of 7)

- [Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding](https://mlsanity.com/paper/2111.15363/) (2021)
- [MVTN: Learning Multi-View Transformations for 3D Understanding](https://mlsanity.com/paper/2212.13462/) (2022)
- [Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study](https://mlsanity.com/paper/2108.01495/) (2021)
- [FEC: Fast Euclidean Clustering for Point Cloud Segmentation](https://mlsanity.com/paper/2208.07678/) (2022)
- [You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning](https://mlsanity.com/paper/2211.09302/) (2022)
- [3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation](https://mlsanity.com/paper/2505.03300/) (2025)
- [Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation](https://mlsanity.com/paper/2505.15545/) (2025)

## Abstract (arXiv)

Object segmentation in three-dimensional (3-D) point clouds is a critical task for robots capable of 3-D perception. Despite the impressive performance of deep learning-based approaches on object segmentation in 2-D images, deep learning has not been applied nearly as successfully for 3-D point cloud segmentation. Deep networks generally require large amounts of labeled training data, which are readily available for 2-D images but are difficult to produce for 3-D point clouds. In this letter, we present Label Diffusion Lidar Segmentation (LDLS), a novel approach for 3-D point cloud segmentation, which leverages 2-D segmentation of an RGB image from an aligned camera to avoid the need for training on annotated 3-D data. We obtain 2-D segmentation predictions by applying Mask-RCNN to the RGB image, and then link this image to a 3-D lidar point cloud by building a graph of connections among 3-D points and 2-D pixels. This graph then directs a semi-supervised label diffusion process, where the 2-D pixels act as source nodes that diffuse object label information through the 3-D point cloud, resulting in a complete 3-D point cloud segmentation. We conduct empirical studies on the KITTI benchmark dataset and on a mobile robot, demonstrating wide applicability and superior performance of LDLS compared with the previous state of the art in 3-D point cloud segmentation, without any need for either 3-D training data or fine tuning of the 2-D image segmentation model.