# YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud

arXiv: https://arxiv.org/abs/1808.02350 · HTML: https://mlsanity.com/paper/1808.02350/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 30 of 32)

- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2203.11139/) (2022)
- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://mlsanity.com/paper/2103.15297/) (2021)
- [Delving into the Devils of Bird's-eye-view Perception: A Review, Evaluation and Recipe](https://mlsanity.com/paper/2209.05324/) (2022)
- [OpenEMMA: Open-Source Multimodal Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2412.15208/) (2024)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensor](https://mlsanity.com/paper/2108.05560/) (2021)
- [Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library](https://mlsanity.com/paper/2211.15975/) (2022)
- [3DGS-DET: Empower 3D Gaussian Splatting with Boundary Guidance and Box-Focused Sampling for Indoor 3D Object Detection](https://mlsanity.com/paper/2410.01647/) (2024)
- [SuperServe: Fine-Grained Inference Serving for Unpredictable Workloads](https://mlsanity.com/paper/2312.16733/) (2023)
- [INSTA-YOLO: Real-Time Instance Segmentation](https://mlsanity.com/paper/2102.06777/) (2021)
- [Optimizing the Placement of Roadside LiDARs for Autonomous Driving](https://mlsanity.com/paper/2310.07247/) (2023)
- [Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications](https://mlsanity.com/paper/2301.00752/) (2023)
- [CAMOT: Camera Angle-aware Multi-Object Tracking](https://mlsanity.com/paper/2409.17533/) (2024)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer](https://mlsanity.com/paper/2308.16518/) (2023)
- [IC-FPS: Instance-Centroid Faster Point Sampling Module for 3D Point-base Object Detection](https://mlsanity.com/paper/2303.17921/) (2023)
- [Composition and Application of Current Advanced Driving Assistance System: A Review](https://mlsanity.com/paper/2105.12348/) (2021)
- [Subgraph Stationary Hardware-Software Inference Co-Design](https://mlsanity.com/paper/2306.17266/) (2023)
- [PV-SSD: A Multi-Modal Point Cloud Feature Fusion Method for Projection Features and Variable Receptive Field Voxel Features](https://mlsanity.com/paper/2308.06791/) (2023)
- [Multimodal and Multiview Deep Fusion for Autonomous Marine Navigation](https://mlsanity.com/paper/2505.01615/) (2025)
- [Efficiency Robustness of Dynamic Deep Learning Systems](https://mlsanity.com/paper/2506.10831/) (2025)
- [Towards Safe, Real-Time Systems: Stereo vs Images and LiDAR for 3D Object Detection](https://mlsanity.com/paper/2202.12773/) (2022)
- [Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection](https://mlsanity.com/paper/2204.09804/) (2022)
- [CompleteDT: Point Cloud Completion with Dense Augment Inference Transformers](https://mlsanity.com/paper/2205.14999/) (2022)
- [Towards Semi-automatic Detection and Localization of Indoor Accessibility Issues using Mobile Depth Scanning and Computer Vision](https://mlsanity.com/paper/2210.02533/) (2022)
- [Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time](https://mlsanity.com/paper/2401.09049/) (2024)
- [PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilation](https://mlsanity.com/paper/2502.07822/) (2025)
- [Rethink 3D Object Detection from Physical World](https://mlsanity.com/paper/2507.00190/) (2025)
- [FractalCloud: A Fractal-Inspired Architecture for Efficient Large-Scale Point Cloud Processing](https://mlsanity.com/paper/2511.07665/) (2025)

## Abstract (arXiv)

Object detection and classification in 3D is a key task in Automated Driving (AD). LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge. In this paper, we build on the success of the one-shot regression meta-architecture in the 2D perspective image space and extend it to generate oriented 3D object bounding boxes from LiDAR point cloud. Our main contribution is in extending the loss function of YOLO v2 to include the yaw angle, the 3D box center in Cartesian coordinates and the height of the box as a direct regression problem. This formulation enables real-time performance, which is essential for automated driving. Our results are showing promising figures on KITTI benchmark, achieving real-time performance (40 fps) on Titan X GPU.