# A General Pipeline for 3D Detection of Vehicles

arXiv: https://arxiv.org/abs/1803.00387 · HTML: https://mlsanity.com/paper/1803.00387/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

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

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation](https://mlsanity.com/paper/2108.06709/) (2021)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection](https://mlsanity.com/paper/2206.15157/) (2022)
- [Pointillism: Accurate 3D bounding box estimation with multi-radars](https://mlsanity.com/paper/2203.04440/) (2022)
- [See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation](https://mlsanity.com/paper/2111.09450/) (2021)
- [HFT: Lifting Perspective Representations via Hybrid Feature Transformation](https://mlsanity.com/paper/2204.05068/) (2022)
- [Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection](https://mlsanity.com/paper/2212.05265/) (2022)
- [PF3Det: A Prompted Foundation Feature Assisted Visual LiDAR 3D Detector](https://mlsanity.com/paper/2504.03563/) (2025)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles](https://mlsanity.com/paper/2409.20426/) (2024)
- [Integrating Multi-Modal Sensors: A Review of Fusion Techniques for Intelligent Vehicles](https://mlsanity.com/paper/2506.21885/) (2025)
- [Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2202.11231/) (2022)
- [ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection](https://mlsanity.com/paper/2211.09518/) (2022)

## Abstract (arXiv)

Autonomous driving requires 3D perception of vehicles and other objects in the in environment. Much of the current methods support 2D vehicle detection. This paper proposes a flexible pipeline to adopt any 2D detection network and fuse it with a 3D point cloud to generate 3D information with minimum changes of the 2D detection networks. To identify the 3D box, an effective model fitting algorithm is developed based on generalised car models and score maps. A two-stage convolutional neural network (CNN) is proposed to refine the detected 3D box. This pipeline is tested on the KITTI dataset using two different 2D detection networks. The 3D detection results based on these two networks are similar, demonstrating the flexibility of the proposed pipeline. The results rank second among the 3D detection algorithms, indicating its competencies in 3D detection.