# Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling

arXiv: https://arxiv.org/abs/2108.06649 · HTML: https://mlsanity.com/paper/2108.06649/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 21 of 21)

- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud](https://mlsanity.com/paper/1812.04244/) (2018)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [PIXOR: Real-time 3D Object Detection from Point Clouds](https://mlsanity.com/paper/1902.06326/) (2019)
- [Joint 3D Proposal Generation and Object Detection from View Aggregation](https://mlsanity.com/paper/1712.02294/) (2017)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [ImVoteNet: Boosting 3D Object Detection in Point Clouds with Image Votes](https://mlsanity.com/paper/2001.10692/) (2020)
- [SESS: Self-Ensembling Semi-Supervised 3D Object Detection](https://mlsanity.com/paper/1912.11803/) (2019)
- [Context-Aware Mixup for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.03557/) (2021)
- [DMT: Dynamic Mutual Training for Semi-Supervised Learning](https://mlsanity.com/paper/2004.08514/) (2020)
- [Uncertainty-Aware Consistency Regularization for Cross-Domain Semantic Segmentation](https://mlsanity.com/paper/2004.08878/) (2020)
- [VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation](https://mlsanity.com/paper/1904.08963/) (2019)
- [Self-Adversarial Disentangling for Specific Domain Adaptation](https://mlsanity.com/paper/2108.03553/) (2021)
- [Computer Stereo Vision for Autonomous Driving](https://mlsanity.com/paper/2012.03194/) (2020)
- [Dynamic Object Tracking and Masking for Visual SLAM](https://mlsanity.com/paper/2008.00072/) (2020)

## Built on by (most cited first, 14 of 14)

- [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://mlsanity.com/paper/2303.02314/) (2023)
- [Generative Domain Adaptation for Face Anti-Spoofing](https://mlsanity.com/paper/2207.10015/) (2022)
- [Test-Time Domain Generalization for Face Anti-Spoofing](https://mlsanity.com/paper/2403.19334/) (2024)
- [BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model](https://mlsanity.com/paper/2401.02317/) (2024)
- [Diverse Target and Contribution Scheduling for Domain Generalization](https://mlsanity.com/paper/2309.16460/) (2023)
- [Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection](https://mlsanity.com/paper/2405.17422/) (2024)
- [Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation](https://mlsanity.com/paper/2312.08234/) (2023)
- [Not Every Side Is Equal: Localization Uncertainty Estimation for Semi-Supervised 3D Object Detection](https://mlsanity.com/paper/2312.10390/) (2023)
- [Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models](https://mlsanity.com/paper/2405.16940/) (2024)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [Boosting Semi-Supervised 3D Object Detection with Semi-Sampling](https://mlsanity.com/paper/2211.07084/) (2022)
- [PC2: Pseudo-Classification Based Pseudo-Captioning for Noisy Correspondence Learning in Cross-Modal Retrieval](https://mlsanity.com/paper/2408.01349/) (2024)
- [JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Data](https://mlsanity.com/paper/2503.08422/) (2025)
- [Source-Free Test-Time Adaptation For Online Surface-Defect Detection](https://mlsanity.com/paper/2408.09494/) (2024)

## Abstract (arXiv)

3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. Especially for outdoor scenes, the problem becomes more severe due to the sparseness of the point cloud and the complexity of urban scenes. Semi-supervised learning is a promising technique to mitigate the data annotation issue. Inspired by this, we propose a novel semi-supervised framework based on pseudo-labeling for outdoor 3D object detection tasks. We design the Adaptive Class Confidence Selection module (ACCS) to generate high-quality pseudo-labels. Besides, we propose Holistic Point Cloud Augmentation (HPCA) for unlabeled data to improve robustness. Experiments on the KITTI benchmark demonstrate the effectiveness of our method.