# AD-PT: Autonomous Driving Pre-Training with Large-scale Point Cloud Dataset

arXiv: https://arxiv.org/abs/2306.00612 · HTML: https://mlsanity.com/paper/2306.00612/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 35 references that are in the map.
- First posted on arXiv 1 Jun 2023.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [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)
- [PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding](https://mlsanity.com/paper/2007.10985/) (2020)
- [Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection](https://mlsanity.com/paper/2012.15712/) (2020)
- [From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network](https://mlsanity.com/paper/1907.03670/) (2019)
- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [STD: Sparse-to-Dense 3D Object Detector for Point Cloud](https://mlsanity.com/paper/1907.10471/) (2019)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [One Million Scenes for Autonomous Driving: ONCE Dataset](https://mlsanity.com/paper/2106.11037/) (2021)
- [Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds](https://mlsanity.com/paper/2109.00179/) (2021)
- [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2103.05346/) (2021)
- [Train in Germany, Test in The USA: Making 3D Object Detectors Generalize](https://mlsanity.com/paper/2005.08139/) (2020)
- [GD-MAE: Generative Decoder for MAE Pre-training on LiDAR Point Clouds](https://mlsanity.com/paper/2212.03010/) (2022)
- [LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection](https://mlsanity.com/paper/2203.14956/) (2022)
- [VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial Attention](https://mlsanity.com/paper/2203.09704/) (2022)
- [Towards Open-Set Object Detection and Discovery](https://mlsanity.com/paper/2204.05604/) (2022)
- [ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2207.12654/) (2022)
- [Masked Autoencoder for Self-Supervised Pre-training on Lidar Point Clouds](https://mlsanity.com/paper/2207.00531/) (2022)
- [Uni3D: A Unified Baseline for Multi-dataset 3D Object Detection](https://mlsanity.com/paper/2303.06880/) (2023)
- [MV-JAR: Masked Voxel Jigsaw and Reconstruction for LiDAR-Based Self-Supervised Pre-Training](https://mlsanity.com/paper/2303.13510/) (2023)
- [Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection](https://mlsanity.com/paper/2303.05886/) (2023)
- [BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving Scenarios](https://mlsanity.com/paper/2212.05758/) (2022)
- [MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds](https://mlsanity.com/paper/2212.07207/) (2022)
- [CO^3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving](https://mlsanity.com/paper/2206.04028/) (2022)

## Built on by (most cited first, 21 of 21)

- [Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities](https://mlsanity.com/paper/2401.08045/) (2024)
- [Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies](https://mlsanity.com/paper/2401.12888/) (2024)
- [VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving](https://mlsanity.com/paper/2411.14716/) (2024)
- [Applications of Large Scale Foundation Models for Autonomous Driving](https://mlsanity.com/paper/2311.12144/) (2023)
- [ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation](https://mlsanity.com/paper/2309.05527/) (2023)
- [Self-Supervised Representation Learning with Joint Embedding Predictive Architecture for Automotive LiDAR Object Detection](https://mlsanity.com/paper/2501.04969/) (2025)
- [T-MAE: Temporal Masked Autoencoders for Point Cloud Representation Learning](https://mlsanity.com/paper/2312.10217/) (2023)
- [Scaling Multi-Camera 3D Object Detection through Weak-to-Strong Eliciting](https://mlsanity.com/paper/2404.06700/) (2024)
- [Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection](https://mlsanity.com/paper/2405.17422/) (2024)
- [SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations](https://mlsanity.com/paper/2309.10527/) (2023)
- [Revisiting Point Cloud Completion: Are We Ready For The Real-World?](https://mlsanity.com/paper/2411.17580/) (2024)
- [SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving](https://mlsanity.com/paper/2509.16588/) (2025)
- [Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems](https://mlsanity.com/paper/2512.24385/) (2025)
- [Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving](https://mlsanity.com/paper/2406.17265/) (2024)
- [TREND: Unsupervised 3D Representation Learning via Temporal Forecasting for LiDAR Perception](https://mlsanity.com/paper/2412.03054/) (2024)
- [PointDiffuse: A Dual-Conditional Diffusion Model for Enhanced Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2503.06094/) (2025)
- [JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data](https://mlsanity.com/paper/2503.08422/) (2025)
- [Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning](https://mlsanity.com/paper/2411.07742/) (2024)
- [Finetuning Pre-trained Model with Limited Data for LiDAR-based 3D Object Detection by Bridging Domain Gaps](https://mlsanity.com/paper/2410.01319/) (2024)
- [RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network](https://mlsanity.com/paper/2501.03221/) (2025)
- [From Binary to Semantic: Utilizing Large-Scale Binary Occupancy Data for 3D Semantic Occupancy Prediction](https://mlsanity.com/paper/2507.13387/) (2025)

## Abstract (arXiv)

It is a long-term vision for Autonomous Driving (AD) community that the perception models can learn from a large-scale point cloud dataset, to obtain unified representations that can achieve promising results on different tasks or benchmarks. Previous works mainly focus on the self-supervised pre-training pipeline, meaning that they perform the pre-training and fine-tuning on the same benchmark, which is difficult to attain the performance scalability and cross-dataset application for the pre-training checkpoint. In this paper, for the first time, we are committed to building a large-scale pre-training point-cloud dataset with diverse data distribution, and meanwhile learning generalizable representations from such a diverse pre-training dataset. We formulate the point-cloud pre-training task as a semi-supervised problem, which leverages the few-shot labeled and massive unlabeled point-cloud data to generate the unified backbone representations that can be directly applied to many baseline models and benchmarks, decoupling the AD-related pre-training process and downstream fine-tuning task. During the period of backbone pre-training, by enhancing the scene- and instance-level distribution diversity and exploiting the backbone's ability to learn from unknown instances, we achieve significant performance gains on a series of downstream perception benchmarks including Waymo, nuScenes, and KITTI, under different baseline models like PV-RCNN++, SECOND, CenterPoint.