# 3D Fully Convolutional Network for Vehicle Detection in Point Cloud

arXiv: https://arxiv.org/abs/1611.08069 · HTML: https://mlsanity.com/paper/1611.08069/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2016.

## Built on by (most cited first, 30 of 57)

- [Offboard 3D Object Detection from Point Cloud Sequences](https://mlsanity.com/paper/2103.05073/) (2021)
- [RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2103.10039/) (2021)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [LiDAR R-CNN: An Efficient and Universal 3D Object Detector](https://mlsanity.com/paper/2103.15297/) (2021)
- [Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2203.10314/) (2022)
- [FCAF3D: Fully Convolutional Anchor-Free 3D Object Detection](https://mlsanity.com/paper/2112.00322/) (2021)
- [LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving](https://mlsanity.com/paper/2101.06547/) (2021)
- [Multi-Modal 3D Object Detection in Autonomous Driving: a Survey](https://mlsanity.com/paper/2106.12735/) (2021)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images](https://mlsanity.com/paper/2205.13764/) (2022)
- [RangeLDM: Fast Realistic LiDAR Point Cloud Generation](https://mlsanity.com/paper/2403.10094/) (2024)
- [4D-Net for Learned Multi-Modal Alignment](https://mlsanity.com/paper/2109.01066/) (2021)
- [UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View](https://mlsanity.com/paper/2303.15083/) (2023)
- [Auto4D: Learning to Label 4D Objects from Sequential Point Clouds](https://mlsanity.com/paper/2101.06586/) (2021)
- [Super Sparse 3D Object Detection](https://mlsanity.com/paper/2301.02562/) (2023)
- [AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving](https://mlsanity.com/paper/2302.08646/) (2023)
- [Fully Sparse Fusion for 3D Object Detection](https://mlsanity.com/paper/2304.12310/) (2023)
- [Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling](https://mlsanity.com/paper/2307.07944/) (2023)
- [PillarGrid: Deep Learning-based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR](https://mlsanity.com/paper/2203.06319/) (2022)
- [Revisiting 3D Object Detection From an Egocentric Perspective](https://mlsanity.com/paper/2112.07787/) (2021)
- [See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation](https://mlsanity.com/paper/2111.09450/) (2021)
- [HyperDet3D: Learning a Scene-conditioned 3D Object Detector](https://mlsanity.com/paper/2204.05599/) (2022)
- [itKD: Interchange Transfer-based Knowledge Distillation for 3D Object Detection](https://mlsanity.com/paper/2205.15531/) (2022)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences](https://mlsanity.com/paper/2306.03206/) (2023)
- [SIDE: Center-based Stereo 3D Detector with Structure-aware Instance Depth Estimation](https://mlsanity.com/paper/2108.09663/) (2021)
- [AGO-Net: Association-Guided 3D Point Cloud Object Detection Network](https://mlsanity.com/paper/2208.11658/) (2022)
- [Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery](https://mlsanity.com/paper/2411.02136/) (2024)
- [Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues](https://mlsanity.com/paper/2102.03602/) (2021)

## Abstract (arXiv)

2D fully convolutional network has been recently successfully applied to object detection from images. In this paper, we extend the fully convolutional network based detection techniques to 3D and apply it to point cloud data. The proposed approach is verified on the task of vehicle detection from lidar point cloud for autonomous driving. Experiments on the KITTI dataset shows a significant performance improvement over the previous point cloud based detection approaches.