# Learning to Detect Mobile Objects from LiDAR Scans Without Labels

arXiv: https://arxiv.org/abs/2203.15882 · HTML: https://mlsanity.com/paper/2203.15882/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 71 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 30 of 41)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Self-training with Noisy Student improves ImageNet classification](https://mlsanity.com/paper/1911.04252/) (2019)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Frustum PointNets for 3D Object Detection from RGB-D Data](https://mlsanity.com/paper/1711.08488/) (2017)
- [3DSSD: Point-based 3D Single Stage Object Detector](https://mlsanity.com/paper/2002.10187/) (2020)
- [Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?](https://mlsanity.com/paper/2003.11539/) (2020)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring](https://mlsanity.com/paper/1911.09785/) (2019)
- [Identifying Mislabeled Data using the Area Under the Margin Ranking](https://mlsanity.com/paper/2001.10528/) (2020)
- [Progressive Feature Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1811.08585/) (2018)
- [Instance Adaptive Self-Training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2008.12197/) (2020)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)
- [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2103.05346/) (2021)
- [Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation](https://mlsanity.com/paper/1910.13049/) (2019)
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://mlsanity.com/paper/2005.08139/) (2020)
- [Learning Features by Watching Objects Move](https://mlsanity.com/paper/1612.06370/) (2016)
- [Multi-Task Self-Training for Learning General Representations](https://mlsanity.com/paper/2108.11353/) (2021)
- [Self-training for Few-shot Transfer Across Extreme Task Differences](https://mlsanity.com/paper/2010.07734/) (2020)

## Built on by (most cited first, 30 of 44)

- [Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling](https://mlsanity.com/paper/2307.07944/) (2023)
- [Towards Unsupervised Object Detection From LiDAR Point Clouds](https://mlsanity.com/paper/2311.02007/) (2023)
- [LISO: Lidar-only Self-Supervised 3D Object Detection](https://mlsanity.com/paper/2403.07071/) (2024)
- [UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes](https://mlsanity.com/paper/2405.15688/) (2024)
- [ADCNet: Learning from Raw Radar Data via Distillation](https://mlsanity.com/paper/2303.11420/) (2023)
- [MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detection](https://mlsanity.com/paper/2308.05988/) (2023)
- [Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery](https://mlsanity.com/paper/2310.19080/) (2023)
- [Unsupervised Adaptation from Repeated Traversals for Autonomous Driving](https://mlsanity.com/paper/2303.15286/) (2023)
- [Commonsense Prototype for Outdoor Unsupervised 3D Object Detection](https://mlsanity.com/paper/2404.16493/) (2024)
- [On the Efficacy of 3D Point Cloud Reinforcement Learning](https://mlsanity.com/paper/2306.06799/) (2023)
- [Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection](https://mlsanity.com/paper/2406.10115/) (2024)
- [Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection](https://mlsanity.com/paper/2408.00619/) (2024)
- [Memorize What Matters: Emergent Scene Decomposition from Multitraverse](https://mlsanity.com/paper/2405.17187/) (2024)
- [LeAP: Consistent multi-domain 3D labeling using Foundation Models](https://mlsanity.com/paper/2502.03901/) (2025)
- [LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds](https://mlsanity.com/paper/2311.01444/) (2023)
- [Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection](https://mlsanity.com/paper/2404.19384/) (2024)
- [DiffuBox: Refining 3D Object Detection with Point Diffusion](https://mlsanity.com/paper/2405.16034/) (2024)
- [Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene](https://mlsanity.com/paper/2407.08569/) (2024)
- [Learning to Detect Objects from Multi-Agent LiDAR Scans without Manual Labels](https://mlsanity.com/paper/2503.08421/) (2025)
- [OpenBox: Annotate Any Bounding Boxes in 3D](https://mlsanity.com/paper/2512.01352/) (2025)
- [Better Monocular 3D Detectors with LiDAR from the Past](https://mlsanity.com/paper/2404.05139/) (2024)
- [Learning 3D Perception from Others' Predictions](https://mlsanity.com/paper/2410.02646/) (2024)
- [HyperMODEST: Self-Supervised 3D Object Detection with Confidence Score Filtering](https://mlsanity.com/paper/2304.14446/) (2023)
- [Label-Efficient 3D Object Detection For Road-Side Units](https://mlsanity.com/paper/2404.06256/) (2024)
- [Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection](https://mlsanity.com/paper/2408.03790/) (2024)
- [PSA-SSL: Pose and Size-aware Self-Supervised Learning on LiDAR Point Clouds](https://mlsanity.com/paper/2503.13914/) (2025)
- [Weakly Supervised 3D Object Detection with Multi-Stage Generalization](https://mlsanity.com/paper/2306.05418/) (2023)
- [HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes](https://mlsanity.com/paper/2403.02769/) (2024)
- [MOD-UV: Learning Mobile Object Detectors from Unlabeled Videos](https://mlsanity.com/paper/2405.14841/) (2024)
- [MonoSOWA: Scalable monocular 3D Object detector Without human Annotations](https://mlsanity.com/paper/2501.09481/) (2025)

## Abstract (arXiv)

Current 3D object detectors for autonomous driving are almost entirely trained on human-annotated data. Although of high quality, the generation of such data is laborious and costly, restricting them to a few specific locations and object types. This paper proposes an alternative approach entirely based on unlabeled data, which can be collected cheaply and in abundance almost everywhere on earth. Our approach leverages several simple common sense heuristics to create an initial set of approximate seed labels. For example, relevant traffic participants are generally not persistent across multiple traversals of the same route, do not fly, and are never under ground. We demonstrate that these seed labels are highly effective to bootstrap a surprisingly accurate detector through repeated self-training without a single human annotated label.