# DUFOMap: Efficient Dynamic Awareness Mapping

arXiv: https://arxiv.org/abs/2403.01449 · HTML: https://mlsanity.com/paper/2403.01449/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 3 Mar 2024.

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

- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://mlsanity.com/paper/2203.11139/) (2022)
- [Differentiable Raycasting for Self-supervised Occupancy Forecasting](https://mlsanity.com/paper/2210.01917/) (2022)
- [Dynamic 3D Scene Analysis by Point Cloud Accumulation](https://mlsanity.com/paper/2207.12394/) (2022)
- [Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions](https://mlsanity.com/paper/2206.04129/) (2022)
- [DeFlow: Decoder of Scene Flow Network in Autonomous Driving](https://mlsanity.com/paper/2401.16122/) (2024)
- [ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building](https://mlsanity.com/paper/2103.04316/) (2021)
- [Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation](https://mlsanity.com/paper/2207.02201/) (2022)
- [Hilti-Oxford Dataset: A Millimetre-Accurate Benchmark for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2208.09825/) (2022)
- [Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments](https://mlsanity.com/paper/2209.13172/) (2022)
- [FusionPortable: A Multi-Sensor Campus-Scene Dataset for Evaluation of Localization and Mapping Accuracy on Diverse Platforms](https://mlsanity.com/paper/2208.11865/) (2022)
- [Building Volumetric Beliefs for Dynamic Environments Exploiting Map-Based Moving Object Segmentation](https://mlsanity.com/paper/2307.08314/) (2023)
- [Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments](https://mlsanity.com/paper/2304.10049/) (2023)
- [UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown](https://mlsanity.com/paper/2003.04749/) (2020)
- [Dynamic Object Aware LiDAR SLAM based on Automatic Generation of Training Data](https://mlsanity.com/paper/2104.03657/) (2021)
- [A Dynamic Points Removal Benchmark in Point Cloud Maps](https://mlsanity.com/paper/2307.07260/) (2023)

## Built on by (most cited first, 11 of 11)

- [SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving](https://mlsanity.com/paper/2407.01702/) (2024)
- [Flow4D: Leveraging 4D Voxel Network for LiDAR Scene Flow Estimation](https://mlsanity.com/paper/2407.07995/) (2024)
- [HiMo: High-Speed Objects Motion Compensation in Point Clouds](https://mlsanity.com/paper/2503.00803/) (2025)
- [VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow](https://mlsanity.com/paper/2503.22328/) (2025)
- [BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps](https://mlsanity.com/paper/2405.07283/) (2024)
- [TeFlow: Enabling Multi-frame Supervision for Self-Supervised Feed-forward Scene Flow Estimation](https://mlsanity.com/paper/2602.19053/) (2026)
- [DoGFlow: Self-Supervised LiDAR Scene Flow via Cross-Modal Doppler Guidance](https://mlsanity.com/paper/2508.18506/) (2025)
- [HIF: Height Interval Filtering for Efficient Dynamic Points Removal](https://mlsanity.com/paper/2503.06863/) (2025)
- [LT-Exosense: A Vision-centric Multi-session Mapping System for Lifelong Safe Navigation of Exoskeletons](https://mlsanity.com/paper/2510.22164/) (2025)
- [OpenNavMap: Multi-Session Appearance-Based Topometric Mapping for Scalable Visual Navigation](https://mlsanity.com/paper/2601.12291/) (2026)
- [Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation](https://mlsanity.com/paper/2605.16922/) (2026)

## Abstract (arXiv)

The dynamic nature of the real world is one of the main challenges in robotics. The first step in dealing with it is to detect which parts of the world are dynamic. A typical benchmark task is to create a map that contains only the static part of the world to support, for example, localization and planning. Current solutions are often applied in post-processing, where parameter tuning allows the user to adjust the setting for a specific dataset. In this paper, we propose DUFOMap, a novel dynamic awareness mapping framework designed for efficient online processing. Despite having the same parameter settings for all scenarios, it performs better or is on par with state-of-the-art methods. Ray casting is utilized to identify and classify fully observed empty regions. Since these regions have been observed empty, it follows that anything inside them at another time must be dynamic. Evaluation is carried out in various scenarios, including outdoor environments in KITTI and Argoverse 2, open areas on the KTH campus, and with different sensor types. DUFOMap outperforms the state of the art in terms of accuracy and computational efficiency. The source code, benchmarks, and links to the datasets utilized are provided. See https://kth-rpl.github.io/dufomap for more details.