# Real3D-Aug: Point Cloud Augmentation by Placing Real Objects with Occlusion Handling for 3D Detection and Segmentation

arXiv: https://arxiv.org/abs/2206.07634 · HTML: https://mlsanity.com/paper/2206.07634/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [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)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://mlsanity.com/paper/2007.16100/) (2020)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [Unbiased Teacher for Semi-Supervised Object Detection](https://mlsanity.com/paper/2102.09480/) (2021)
- [SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation](https://mlsanity.com/paper/2004.01803/) (2020)
- [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2103.05346/) (2021)
- [3D Object Proposals using Stereo Imagery for Accurate Object Class Detection](https://mlsanity.com/paper/1608.07711/) (2016)
- [Improving 3D Object Detection through Progressive Population Based Augmentation](https://mlsanity.com/paper/2004.00831/) (2020)
- [LiDAR Sensor modeling and Data augmentation with GANs for Autonomous driving](https://mlsanity.com/paper/1905.07290/) (2019)
- [Quantifying Data Augmentation for LiDAR based 3D Object Detection](https://mlsanity.com/paper/2004.01643/) (2020)
- [Unsupervised Neural Sensor Models for Synthetic LiDAR Data Augmentation](https://mlsanity.com/paper/1911.10575/) (2019)

## Built on by (most cited first, 4 of 4)

- [Context-Aware Data Augmentation for LIDAR 3D Object Detection](https://mlsanity.com/paper/2211.10850/) (2022)
- [Text3DAug -- Prompted Instance Augmentation for LiDAR Perception](https://mlsanity.com/paper/2408.14253/) (2024)
- [D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes](https://mlsanity.com/paper/2404.11127/) (2024)
- [Point Cloud Recombination: Systematic Real Data Augmentation Using Robotic Targets for LiDAR Perception Validation](https://mlsanity.com/paper/2505.02476/) (2025)

## Abstract (arXiv)

Object detection and semantic segmentation with the 3D lidar point cloud data require expensive annotation. We propose a data augmentation method that takes advantage of already annotated data multiple times. We propose an augmentation framework that reuses real data, automatically finds suitable placements in the scene to be augmented, and handles occlusions explicitly. Due to the usage of the real data, the scan points of newly inserted objects in augmentation sustain the physical characteristics of the lidar, such as intensity and raydrop. The pipeline proves competitive in training top-performing models for 3D object detection and semantic segmentation. The new augmentation provides a significant performance gain in rare and essential classes, notably 6.65% average precision gain for "Hard" pedestrian class in KITTI object detection or 2.14 mean IoU gain in the SemanticKITTI segmentation challenge over the state of the art.