# Weakly Supervised 3D Object Detection from Point Clouds

arXiv: https://arxiv.org/abs/2007.13970 · HTML: https://mlsanity.com/paper/2007.13970/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 30 of 41)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers](https://mlsanity.com/paper/2104.11896/) (2021)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)](https://mlsanity.com/paper/2108.05888/) (2021)
- [3D Spatial Recognition without Spatially Labeled 3D](https://mlsanity.com/paper/2105.06461/) (2021)
- [FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection](https://mlsanity.com/paper/2105.07647/) (2021)
- [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://mlsanity.com/paper/2305.19812/) (2023)
- [WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection](https://mlsanity.com/paper/2203.08332/) (2022)
- [Weakly Supervised Monocular 3D Object Detection using Multi-View Projection and Direction Consistency](https://mlsanity.com/paper/2303.08686/) (2023)
- [Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach](https://mlsanity.com/paper/2101.06931/) (2021)
- [Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement](https://mlsanity.com/paper/2203.05238/) (2022)
- [Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud](https://mlsanity.com/paper/2108.03634/) (2021)
- [Multimodal Transformer for Automatic 3D Annotation and Object Detection](https://mlsanity.com/paper/2207.09805/) (2022)
- [KSS-ICP: Point Cloud Registration based on Kendall Shape Space](https://mlsanity.com/paper/2211.02807/) (2022)
- [Weakly Supervised Monocular 3D Detection with a Single-View Image](https://mlsanity.com/paper/2402.19144/) (2024)
- [MAP-Gen: An Automated 3D-Box Annotation Flow with Multimodal Attention Point Generator](https://mlsanity.com/paper/2203.15700/) (2022)
- [WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels](https://mlsanity.com/paper/2209.11523/) (2022)
- [Weakly Supervised 3D Object Detection via Multi-Level Visual Guidance](https://mlsanity.com/paper/2312.07530/) (2023)
- [MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2401.16305/) (2024)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across Objects and Views](https://mlsanity.com/paper/2109.07945/) (2021)
- [LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds](https://mlsanity.com/paper/2311.01444/) (2023)
- [General Geometry-aware Weakly Supervised 3D Object Detection](https://mlsanity.com/paper/2407.13748/) (2024)
- [VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection](https://mlsanity.com/paper/2404.00149/) (2024)
- [Segment, Lift and Fit: Automatic 3D Shape Labeling from 2D Prompts](https://mlsanity.com/paper/2407.11382/) (2024)
- [SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts](https://mlsanity.com/paper/2503.06467/) (2025)
- [Context-Aware Transformer for 3D Point Cloud Automatic Annotation](https://mlsanity.com/paper/2303.14893/) (2023)
- [Every Dataset Counts: Scaling up Monocular 3D Object Detection with Joint Datasets Training](https://mlsanity.com/paper/2310.00920/) (2023)
- [MWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving](https://mlsanity.com/paper/2312.06988/) (2023)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)

## Abstract (arXiv)

A crucial task in scene understanding is 3D object detection, which aims to detect and localize the 3D bounding boxes of objects belonging to specific classes. Existing 3D object detectors heavily rely on annotated 3D bounding boxes during training, while these annotations could be expensive to obtain and only accessible in limited scenarios. Weakly supervised learning is a promising approach to reducing the annotation requirement, but existing weakly supervised object detectors are mostly for 2D detection rather than 3D. In this work, we propose VS3D, a framework for weakly supervised 3D object detection from point clouds without using any ground truth 3D bounding box for training. First, we introduce an unsupervised 3D proposal module that generates object proposals by leveraging normalized point cloud densities. Second, we present a cross-modal knowledge distillation strategy, where a convolutional neural network learns to predict the final results from the 3D object proposals by querying a teacher network pretrained on image datasets. Comprehensive experiments on the challenging KITTI dataset demonstrate the superior performance of our VS3D in diverse evaluation settings. The source code and pretrained models are publicly available at https://github.com/Zengyi-Qin/Weakly-Supervised-3D-Object-Detection.