# 3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

arXiv: https://arxiv.org/abs/2505.03300 · HTML: https://mlsanity.com/paper/2505.03300/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 21 of 21)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1811.12833/) (2018)
- [Point Transformer V3: Simpler, Faster, Stronger](https://mlsanity.com/paper/2312.10035/) (2023)
- [Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1903.04064/) (2019)
- [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2103.05346/) (2021)
- [SAM3D: Segment Anything in 3D Scenes](https://mlsanity.com/paper/2306.03908/) (2023)
- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data](https://mlsanity.com/paper/2203.16258/) (2022)
- [Segment Any Point Cloud Sequences by Distilling Vision Foundation Models](https://mlsanity.com/paper/2306.09347/) (2023)
- [Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels](https://mlsanity.com/paper/2312.17232/) (2023)
- [Learning 3D Semantic Segmentation with only 2D Image Supervision](https://mlsanity.com/paper/2110.11325/) (2021)
- [Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation](https://mlsanity.com/paper/2204.07548/) (2022)
- [Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters](https://mlsanity.com/paper/2107.09783/) (2021)
- [See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data](https://mlsanity.com/paper/2307.10782/) (2023)
- [LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images](https://mlsanity.com/paper/1910.13955/) (2019)
- [Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2312.07221/) (2023)
- [T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds](https://mlsanity.com/paper/2309.08302/) (2023)

## Built on by (most cited first, 1 of 1)

- [Multi-View Projection for Unsupervised Domain Adaptation in 3D Semantic Segmentation](https://mlsanity.com/paper/2505.15545/) (2025)

## Abstract (arXiv)

Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive annotated datasets and faces the problem of domain shifts. We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods, avoiding the need for direct 3D annotation or reliance on additional modalities such as camera images at inference time. Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation to these views using a camera-domain pretrained model. The segmented 2D outputs are then back-projected onto the 3D points, with a simple voting-based estimator that merges the labels associated to each 3D point. Our main contribution is a global pipeline for 3D semantic segmentation requiring no prior 3D annotation and not other modality for inference, which can be used for pseudo-label generation. We conduct a thorough ablation study and demonstrate the potential of the generated pseudo-labels for the Unsupervised Domain Adaptation task.