# A Dynamic Points Removal Benchmark in Point Cloud Maps

arXiv: https://arxiv.org/abs/2307.07260 · HTML: https://mlsanity.com/paper/2307.07260/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 14 of 14)

- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [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)
- [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)
- [Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation](https://mlsanity.com/paper/2201.04501/) (2022)
- [Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments](https://mlsanity.com/paper/2209.13172/) (2022)
- [Robust Odometry and Mapping for Multi-LiDAR Systems with Online Extrinsic Calibration](https://mlsanity.com/paper/2010.14294/) (2020)
- [Dynablox: Real-time Detection of Diverse Dynamic Objects in Complex Environments](https://mlsanity.com/paper/2304.10049/) (2023)
- [SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping](https://mlsanity.com/paper/2211.03900/) (2022)

## Built on by (most cited first, 8 of 8)

- [SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving](https://mlsanity.com/paper/2407.01702/) (2024)
- [DeFlow: Decoder of Scene Flow Network in Autonomous Driving](https://mlsanity.com/paper/2401.16122/) (2024)
- [DUFOMap: Efficient Dynamic Awareness Mapping](https://mlsanity.com/paper/2403.01449/) (2024)
- [3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation](https://mlsanity.com/paper/2405.03388/) (2024)
- [BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps](https://mlsanity.com/paper/2405.07283/) (2024)
- [HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors](https://mlsanity.com/paper/2408.06328/) (2024)
- [DOC-Depth: A novel approach for dense depth ground truth generation](https://mlsanity.com/paper/2502.02144/) (2025)
- [HIF: Height Interval Filtering for Efficient Dynamic Points Removal](https://mlsanity.com/paper/2503.06863/) (2025)

## Abstract (arXiv)

In the field of robotics, the point cloud has become an essential map representation. From the perspective of downstream tasks like localization and global path planning, points corresponding to dynamic objects will adversely affect their performance. Existing methods for removing dynamic points in point clouds often lack clarity in comparative evaluations and comprehensive analysis. Therefore, we propose an easy-to-extend unified benchmarking framework for evaluating techniques for removing dynamic points in maps. It includes refactored state-of-art methods and novel metrics to analyze the limitations of these approaches. This enables researchers to dive deep into the underlying reasons behind these limitations. The benchmark makes use of several datasets with different sensor types. All the code and datasets related to our study are publicly available for further development and utilization.