# StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection

arXiv: https://arxiv.org/abs/2407.03825 · HTML: https://mlsanity.com/paper/2407.03825/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 38 references that are in the map.
- First posted on arXiv 4 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images](https://mlsanity.com/paper/2206.01256/) (2022)
- [V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer](https://mlsanity.com/paper/2203.10638/) (2022)
- [Exploring Object-Centric Temporal Modeling for Efficient Multi-View 3D Object Detection](https://mlsanity.com/paper/2303.11926/) (2023)
- [OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication](https://mlsanity.com/paper/2109.07644/) (2021)
- [V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction](https://mlsanity.com/paper/2008.07519/) (2020)
- [DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection](https://mlsanity.com/paper/2204.05575/) (2022)
- [Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps](https://mlsanity.com/paper/2209.12836/) (2022)
- [F-Cooper: Feature based Cooperative Perception for Autonomous Vehicle Edge Computing System Using 3D Point Clouds](https://mlsanity.com/paper/1909.06459/) (2019)
- [Cooper: Cooperative Perception for Connected Autonomous Vehicles based on 3D Point Clouds](https://mlsanity.com/paper/1905.05265/) (2019)
- [V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Driving](https://mlsanity.com/paper/2202.08449/) (2022)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting](https://mlsanity.com/paper/2305.05938/) (2023)
- [Latency-Aware Collaborative Perception](https://mlsanity.com/paper/2207.08560/) (2022)
- [Spatio-Temporal Domain Awareness for Multi-Agent Collaborative Perception](https://mlsanity.com/paper/2307.13929/) (2023)
- [Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving](https://mlsanity.com/paper/2109.11615/) (2021)
- [QUEST: Query Stream for Practical Cooperative Perception](https://mlsanity.com/paper/2308.01804/) (2023)
- [Cooperative LIDAR Object Detection via Feature Sharing in Deep Networks](https://mlsanity.com/paper/2002.08440/) (2020)
- [QueryProp: Object Query Propagation for High-Performance Video Object Detection](https://mlsanity.com/paper/2207.10959/) (2022)

## Built on by (most cited first, 2 of 2)

- [SparseAlign: A Fully Sparse Framework for Cooperative Object Detection](https://mlsanity.com/paper/2503.12982/) (2025)
- [SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception](https://mlsanity.com/paper/2508.13007/) (2025)

## Abstract (arXiv)

Cooperative perception via communication among intelligent traffic agents has great potential to improve the safety of autonomous driving. However, limited communication bandwidth, localization errors and asynchronized capturing time of sensor data, all introduce difficulties to the data fusion of different agents. To some extend, previous works have attempted to reduce the shared data size, mitigate the spatial feature misalignment caused by localization errors and communication delay. However, none of them have considered the asynchronized sensor ticking times, which can lead to dynamic object misplacement of more than one meter during data fusion. In this work, we propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt widely used dataset OPV2V and DairV2X with considering asynchronous LiDAR sensor ticking times and build an efficient fully sparse framework with modeling the temporal information of individual objects with query-based techniques. The experiment results confirmed the superior efficiency of our fully sparse framework compared to the state-of-the-art dense models. More importantly, they show that the point-wise observation timestamps of the dynamic objects are crucial for accurate modeling the object temporal context and the predictability of their time-related locations. The official code is available at urlhttps://github.com/YuanYunshuang/CoSense3D.