# StrObe: Streaming Object Detection from LiDAR Packets

arXiv: https://arxiv.org/abs/2011.06425 · HTML: https://mlsanity.com/paper/2011.06425/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 17 of 17)

- [MP3: A Unified Model to Map, Perceive, Predict and Plan](https://mlsanity.com/paper/2101.06806/) (2021)
- [Learning from All Vehicles](https://mlsanity.com/paper/2203.11934/) (2022)
- [3D Object Detection for Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2206.09474/) (2022)
- [Auto4D: Learning to Label 4D Objects from Sequential Point Clouds](https://mlsanity.com/paper/2101.06586/) (2021)
- [Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark](https://mlsanity.com/paper/2212.08914/) (2022)
- [Fooling LiDAR Perception via Adversarial Trajectory Perturbation](https://mlsanity.com/paper/2103.15326/) (2021)
- [Deep Multi-Task Learning for Joint Localization, Perception, and Prediction](https://mlsanity.com/paper/2101.06720/) (2021)
- [PolarStream: Streaming Lidar Object Detection and Segmentation with Polar Pillars](https://mlsanity.com/paper/2106.07545/) (2021)
- [MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory](https://mlsanity.com/paper/2311.01556/) (2023)
- [PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection](https://mlsanity.com/paper/2308.03982/) (2023)
- [Multi-modal Streaming 3D Object Detection](https://mlsanity.com/paper/2209.04966/) (2022)
- [Real-time Stereo-based 3D Object Detection for Streaming Perception](https://mlsanity.com/paper/2410.12394/) (2024)
- [Towards Streaming LiDAR Object Detection with Point Clouds as Egocentric Sequences](https://mlsanity.com/paper/2506.06944/) (2025)
- [4DSegStreamer: Streaming 4D Panoptic Segmentation via Dual Threads](https://mlsanity.com/paper/2510.17664/) (2025)
- [SSCATeR: Sparse Scatter-Based Convolution Algorithm with Temporal Data Recycling for Real-Time 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/2512.08557/) (2025)
- [Scene Reconstruction as Mapping Priors for 3D Detection](https://mlsanity.com/paper/2605.22997/) (2026)
- [GraphBEV++: Multi-Modal Feature Alignment for Autonomous Driving](https://mlsanity.com/paper/2606.16354/) (2026)

## Abstract (arXiv)

Many modern robotics systems employ LiDAR as their main sensing modality due to its geometrical richness. Rolling shutter LiDARs are particularly common, in which an array of lasers scans the scene from a rotating base. Points are emitted as a stream of packets, each covering a sector of the 360deg coverage. Modern perception algorithms wait for the full sweep to be built before processing the data, which introduces an additional latency. For typical 10Hz LiDARs this will be 100ms. As a consequence, by the time an output is produced, it no longer accurately reflects the state of the world. This poses a challenge, as robotics applications require minimal reaction times, such that maneuvers can be quickly planned in the event of a safety-critical situation. In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built. StrObe reuses computations from previous packets and iteratively updates a latent spatial representation of the scene, which acts as a memory, as new evidence comes in, resulting in accurate low-latency perception. We demonstrate the effectiveness of our approach on a large scale real-world dataset, showing that StrObe far outperforms the state-of-the-art when latency is taken into account, and matches the performance in the traditional setting.