# Pattern-Aware Data Augmentation for LiDAR 3D Object Detection

arXiv: https://arxiv.org/abs/2112.00050 · HTML: https://mlsanity.com/paper/2112.00050/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 20 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (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)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving](https://mlsanity.com/paper/1906.06310/) (2019)
- [LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World](https://mlsanity.com/paper/2006.09348/) (2020)
- [PointMixup: Augmentation for Point Clouds](https://mlsanity.com/paper/2008.06374/) (2020)
- [PointAugment: an Auto-Augmentation Framework for Point Cloud Classification](https://mlsanity.com/paper/2002.10876/) (2020)
- [AFDet: Anchor Free One Stage 3D Object Detection](https://mlsanity.com/paper/2006.12671/) (2020)
- [Augmented LiDAR Simulator for Autonomous Driving](https://mlsanity.com/paper/1811.07112/) (2018)
- [Improving 3D Object Detection through Progressive Population Based Augmentation](https://mlsanity.com/paper/2004.00831/) (2020)
- [Part-Aware Data Augmentation for 3D Object Detection in Point Cloud](https://mlsanity.com/paper/2007.13373/) (2020)
- [SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2006.04043/) (2020)
- [Quantifying Data Augmentation for LiDAR based 3D Object Detection](https://mlsanity.com/paper/2004.01643/) (2020)

## Built on by (most cited first, 15 of 15)

- [Learning to Generate Realistic LiDAR Point Clouds](https://mlsanity.com/paper/2209.03954/) (2022)
- [Towards Realistic Scene Generation with LiDAR Diffusion Models](https://mlsanity.com/paper/2404.00815/) (2024)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [Instant Domain Augmentation for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2303.14378/) (2023)
- [LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations](https://mlsanity.com/paper/2210.13488/) (2022)
- [Real-Aug: Realistic Scene Synthesis for LiDAR Augmentation in 3D Object Detection](https://mlsanity.com/paper/2305.12853/) (2023)
- [What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation](https://mlsanity.com/paper/2206.09664/) (2022)
- [Context-Aware Data Augmentation for LIDAR 3D Object Detection](https://mlsanity.com/paper/2211.10850/) (2022)
- [Robust Long-Range Perception Against Sensor Misalignment in Autonomous Vehicles](https://mlsanity.com/paper/2408.11196/) (2024)
- [DR.CPO: Diversified and Realistic 3D Augmentation via Iterative Construction, Random Placement, and HPR Occlusion](https://mlsanity.com/paper/2303.12743/) (2023)
- [Semantic Segmentation on 3D Point Clouds with High Density Variations](https://mlsanity.com/paper/2307.01489/) (2023)
- [VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR Data](https://mlsanity.com/paper/2312.08871/) (2023)
- [SurfaceAug: Closing the Gap in Multimodal Ground Truth Sampling](https://mlsanity.com/paper/2312.03808/) (2023)
- [SGCCNet: Single-Stage 3D Object Detector With Saliency-Guided Data Augmentation and Confidence Correction Mechanism](https://mlsanity.com/paper/2407.01239/) (2024)
- [HSDA: High-frequency Shuffle Data Augmentation for Bird's-Eye-View Map Segmentation](https://mlsanity.com/paper/2412.06127/) (2024)

## Abstract (arXiv)

Autonomous driving datasets are often skewed and in particular, lack training data for objects at farther distances from the ego vehicle. The imbalance of data causes a performance degradation as the distance of the detected objects increases. In this paper, we propose pattern-aware ground truth sampling, a data augmentation technique that downsamples an object's point cloud based on the LiDAR's characteristics. Specifically, we mimic the natural diverging point pattern variation that occurs for objects at depth to simulate samples at farther distances. Thus, the network has more diverse training examples and can generalize to detecting farther objects more effectively. We evaluate against existing data augmentation techniques that use point removal or perturbation methods and find that our method outperforms all of them. Additionally, we propose using equal element AP bins to evaluate the performance of 3D object detectors across distance. We improve the performance of PV-RCNN on the car class by more than 0.7 percent on the KITTI validation split at distances greater than 25 m.