# False Positive Sampling-based Data Augmentation for Enhanced 3D Object Detection Accuracy

arXiv: https://arxiv.org/abs/2403.02639 · HTML: https://mlsanity.com/paper/2403.02639/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 35 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 25 of 25)

- [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)
- [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)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (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)
- [PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection](https://mlsanity.com/paper/2102.00463/) (2021)
- [CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images](https://mlsanity.com/paper/1808.01097/) (2018)
- [3D Object Detection for Autonomous Driving: A Survey](https://mlsanity.com/paper/2106.10823/) (2021)
- [PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds](https://mlsanity.com/paper/2208.00223/) (2022)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [An Empirical Exploration of Curriculum Learning for Neural Machine Translation](https://mlsanity.com/paper/1811.00739/) (2018)
- [Curriculum Pre-training for End-to-End Speech Translation](https://mlsanity.com/paper/2004.10093/) (2020)
- [Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding](https://mlsanity.com/paper/1901.01415/) (2019)
- [Point Cloud Augmentation with Weighted Local Transformations](https://mlsanity.com/paper/2110.05379/) (2021)
- [Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification](https://mlsanity.com/paper/2009.13698/) (2020)
- [Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models](https://mlsanity.com/paper/2004.05757/) (2020)
- [Quantifying Data Augmentation for LiDAR based 3D Object Detection](https://mlsanity.com/paper/2004.01643/) (2020)
- [StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification](https://mlsanity.com/paper/2004.12032/) (2020)
- [Multiple Instance Curriculum Learning for Weakly Supervised Object Detection](https://mlsanity.com/paper/1711.09191/) (2017)
- [User interface for in-vehicle systems with on-wheel finger spreading gestures and head-up displays](https://mlsanity.com/paper/2102.10497/) (2021)
- [Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification](https://mlsanity.com/paper/2104.10401/) (2021)

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

- [Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection](https://mlsanity.com/paper/2501.15449/) (2025)

## Abstract (arXiv)

Recent studies have focused on enhancing the performance of 3D object detection models. Among various approaches, ground-truth sampling has been proposed as an augmentation technique to address the challenges posed by limited ground-truth data. However, an inherent issue with ground-truth sampling is its tendency to increase false positives. Therefore, this study aims to overcome the limitations of ground-truth sampling and improve the performance of 3D object detection models by developing a new augmentation technique called false-positive sampling. False-positive sampling involves retraining the model using point clouds that are identified as false positives in the model's predictions. We propose an algorithm that utilizes both ground-truth and false-positive sampling and an algorithm for building the false-positive sample database. Additionally, we analyze the principles behind the performance enhancement due to false-positive sampling. Our experiments demonstrate that models utilizing false-positive sampling show a reduction in false positives and exhibit improved object detection performance. On the KITTI and Waymo Open datasets, models with false-positive sampling surpass the baseline models by a large margin.