# LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting

arXiv: https://arxiv.org/abs/2003.05982 · HTML: https://mlsanity.com/paper/2003.05982/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Multi-Modal Fusion Transformer for End-to-End Autonomous Driving](https://mlsanity.com/paper/2104.09224/) (2021)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks](https://mlsanity.com/paper/2110.04076/) (2021)
- [MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Data](https://mlsanity.com/paper/2104.10772/) (2021)
- [DeTra: A Unified Model for Object Detection and Trajectory Forecasting](https://mlsanity.com/paper/2406.04426/) (2024)
- [Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding](https://mlsanity.com/paper/2202.01478/) (2022)
- [M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving](https://mlsanity.com/paper/2403.12552/) (2024)
- [Joint Perception and Prediction for Autonomous Driving: A Survey](https://mlsanity.com/paper/2412.14088/) (2024)
- [Raising context awareness in motion forecasting](https://mlsanity.com/paper/2109.08048/) (2021)
- [Sequential Point Clouds: A Survey](https://mlsanity.com/paper/2204.09337/) (2022)
- [Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models](https://mlsanity.com/paper/2101.03279/) (2021)
- [Efficient Transformer-based 3D Object Detection with Dynamic Token Halting](https://mlsanity.com/paper/2303.05078/) (2023)
- [MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving](https://mlsanity.com/paper/2405.07573/) (2024)
- [Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Driving](https://mlsanity.com/paper/2210.06758/) (2022)
- [LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning](https://mlsanity.com/paper/2310.13135/) (2023)
- [Real-time Stereo-based 3D Object Detection for Streaming Perception](https://mlsanity.com/paper/2410.12394/) (2024)
- [To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software](https://mlsanity.com/paper/2508.20892/) (2025)
- [What Matters in Range View 3D Object Detection](https://mlsanity.com/paper/2407.16789/) (2024)
- [PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilation](https://mlsanity.com/paper/2502.07822/) (2025)
- [TriBand-BEV: Real-Time LiDAR-Only 3D Pedestrian Detection via Height-Aware BEV and High-Resolution Feature Fusion](https://mlsanity.com/paper/2605.12220/) (2026)
- [Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives](https://mlsanity.com/paper/2608.09541/) (2026)

## Abstract (arXiv)

In this work, we present LaserFlow, an efficient method for 3D object detection and motion forecasting from LiDAR. Unlike the previous work, our approach utilizes the native range view representation of the LiDAR, which enables our method to operate at the full range of the sensor in real-time without voxelization or compression of the data. We propose a new multi-sweep fusion architecture, which extracts and merges temporal features directly from the range images. Furthermore, we propose a novel technique for learning a probability distribution over future trajectories inspired by curriculum learning. We evaluate LaserFlow on two autonomous driving datasets and demonstrate competitive results when compared to the existing state-of-the-art methods.