mlsanityData to 7 Oct 2026

LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting

arXiv 2003.05982 ↗First posted 12 Mar 2020cs.CV cs.LG cs.ROcited by 22

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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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 2; 2024: 6; 2025: 2; 2026: 2.

22 papers.

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  1. 2022TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Drivingcited by 477
  2. 2021Multi-Modal Fusion Transformer for End-to-End Autonomous Drivingcited by 372
  3. 20223D Object Detection for Autonomous Driving: A Comprehensive Surveycited by 132
  4. 2021Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networkscited by 42
  5. 2021MVFuseNet: Improving End-to-End Object Detection and Motion Forecasting through Multi-View Fusion of LiDAR Datacited by 18
  6. 2024DeTra: A Unified Model for Object Detection and Trajectory Forecastingcited by 15
  7. 2022Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encodingcited by 8
  8. 2024M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Drivingcited by 7
  9. 2024Joint Perception and Prediction for Autonomous Driving: A Surveycited by 7
  10. 2021Raising context awareness in motion forecastingcited by 6
  11. 2022Sequential Point Clouds: A Surveycited by 5
  12. 2021Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Modelscited by 3
10 more
  1. 2023Efficient Transformer-based 3D Object Detection with Dynamic Token Haltingcited by 3
  2. 2024MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Drivingcited by 3
  3. 2022Exploring Contextual Representation and Multi-Modality for End-to-End Autonomous Drivingcited by 1
  4. 2023LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learningcited by 1
  5. 2024Real-time Stereo-based 3D Object Detection for Streaming Perceptioncited by 1
  6. 2025To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Softwarecited by 1
  7. 2024What Matters in Range View 3D Object Detectioncited by 0
  8. 2025PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point Dilationcited by 0
  9. 2026TriBand-BEV: Real-Time LiDAR-Only 3D Pedestrian Detection via Height-Aware BEV and High-Resolution Feature Fusioncited by 0
  10. 2026Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectivescited by 0