# Unsupervised UAV 3D Trajectories Estimation with Sparse Point Clouds

arXiv: https://arxiv.org/abs/2412.12716 · HTML: https://mlsanity.com/paper/2412.12716/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 19 of 19)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://mlsanity.com/paper/2007.16100/) (2020)
- [VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation](https://mlsanity.com/paper/1904.08963/) (2019)
- [Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking](https://mlsanity.com/paper/2101.08466/) (2021)
- [Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark](https://mlsanity.com/paper/2103.13933/) (2021)
- [Dogfight: Detecting Drones from Drones Videos](https://mlsanity.com/paper/2103.17242/) (2021)
- [MMAUD: A Comprehensive Multi-Modal Anti-UAV Dataset for Modern Miniature Drone Threats](https://mlsanity.com/paper/2402.03706/) (2024)
- [The 2nd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2108.09909/) (2021)
- [The 3rd Anti-UAV Workshop & Challenge: Methods and Results](https://mlsanity.com/paper/2305.07290/) (2023)
- [A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion](https://mlsanity.com/paper/2109.01800/) (2021)
- [Detection and Tracking of Small Objects in Sparse 3D Laser Range Data](https://mlsanity.com/paper/1903.05889/) (2019)
- [VIRAL-Fusion: A Visual-Inertial-Ranging-Lidar Sensor Fusion Approach](https://mlsanity.com/paper/2010.12274/) (2020)
- [Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds](https://mlsanity.com/paper/2103.04069/) (2021)
- [DIRECT: A Differential Dynamic Programming Based Framework for Trajectory Generation](https://mlsanity.com/paper/2109.04686/) (2021)
- [On Onboard LiDAR-based Flying Object Detection](https://mlsanity.com/paper/2303.05404/) (2023)
- [SPINS: Structure Priors aided Inertial Navigation System](https://mlsanity.com/paper/2012.14053/) (2020)
- [Vision Based Autonomous UAV Plane Estimation And Following for Building Inspection](https://mlsanity.com/paper/2102.01423/) (2021)

## Built on by (most cited first, 2 of 2)

- [Separating Drone Point Clouds From Complex Backgrounds by Cluster Filter -- Technical Report for CVPR 2024 UG2 Challenge](https://mlsanity.com/paper/2412.16947/) (2024)
- [CETUS: Causal Event-Driven Temporal Modeling With Unified Variable-Rate Scheduling](https://mlsanity.com/paper/2509.13784/) (2025)

## Abstract (arXiv)

Compact UAV systems, while advancing delivery and surveillance, pose significant security challenges due to their small size, which hinders detection by traditional methods. This paper presents a cost-effective, unsupervised UAV detection method using spatial-temporal sequence processing to fuse multiple LiDAR scans for accurate UAV tracking in real-world scenarios. Our approach segments point clouds into foreground and background, analyzes spatial-temporal data, and employs a scoring mechanism to enhance detection accuracy. Tested on a public dataset, our solution placed 4th in the CVPR 2024 UG2+ Challenge, demonstrating its practical effectiveness. We plan to open-source all designs, code, and sample data for the research community github.com/lianghanfang/UnLiDAR-UAV-Est.