# MotionSync: Non-Causal Refinement of Causal Tracker for Label-Efficient 3D Perception

arXiv: https://arxiv.org/abs/2608.29567 · HTML: https://mlsanity.com/paper/2608.29567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 30 Aug 2026.

## Builds on (most cited first, 26 of 26)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking](https://mlsanity.com/paper/2203.14360/) (2022)
- [StrongSORT: Make DeepSORT Great Again](https://mlsanity.com/paper/2202.13514/) (2022)
- [Virtual Sparse Convolution for Multimodal 3D Object Detection](https://mlsanity.com/paper/2303.02314/) (2023)
- [Score refinement for confidence-based 3D multi-object tracking](https://mlsanity.com/paper/2107.04327/) (2021)
- [Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking](https://mlsanity.com/paper/2307.16675/) (2023)
- [DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds](https://mlsanity.com/paper/2306.06023/) (2023)
- [Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection](https://mlsanity.com/paper/2304.12315/) (2023)
- [TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM](https://mlsanity.com/paper/2210.16204/) (2022)
- [Challenges in Designing Datasets and Validation for Autonomous Driving](https://mlsanity.com/paper/1901.09270/) (2019)
- [Monocular Instance Motion Segmentation for Autonomous Driving: KITTI InstanceMotSeg Dataset and Multi-task Baseline](https://mlsanity.com/paper/2008.07008/) (2020)
- [MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving](https://mlsanity.com/paper/2409.16149/) (2024)
- [Adversarial Attacks on Multi-task Visual Perception for Autonomous Driving](https://mlsanity.com/paper/2107.07449/) (2021)
- [LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds](https://mlsanity.com/paper/2308.09908/) (2023)
- [RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud](https://mlsanity.com/paper/2405.11536/) (2024)
- [Offline Tracking with Object Permanence](https://mlsanity.com/paper/2310.01288/) (2023)
- [BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data](https://mlsanity.com/paper/2406.18414/) (2024)
- [OptFlow: Fast Optimization-based Scene Flow Estimation without Supervision](https://mlsanity.com/paper/2401.02550/) (2024)
- [HybridTrack: A Hybrid Approach for Robust Multi-Object Tracking](https://mlsanity.com/paper/2501.01275/) (2025)
- [Bayesian Multiobject Tracking With Neural-Enhanced Motion and Measurement Models](https://mlsanity.com/paper/2506.18124/) (2025)
- [CrossTracker: Robust Multi-modal 3D Multi-Object Tracking via Cross Correction](https://mlsanity.com/paper/2411.18850/) (2024)
- [Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection](https://mlsanity.com/paper/2505.16029/) (2025)
- [Offline-Poly: A Polyhedral Framework For Offline 3D Multi-Object Tracking](https://mlsanity.com/paper/2602.13772/) (2026)

## Abstract (arXiv)

Three-dimensional box-and-track annotation is the cost bottleneck in autonomous-driving data engines, and the offline systems built to relieve it replace the online perception stack outright, so a team needing both regimes maintains and reconciles two. MotionSync makes the causal/non-causal boundary an explicit architectural seam instead. A strictly causal tracker, built on a strong published baseline and extended with innovation-driven uncertainty calibration, frame-rate-invariant kinematic association gates, and multi-hypothesis motion with learned mode selection, emits a valid online result. A non-causal pass then revises the buffered trajectories with Rauch--Tung--Striebel smoothing applied separately to pose, extent and yaw, physics-validated gap completion, and semantic pruning of ghost tracks against LiDAR point labels. The refiner never writes back, so one system serves both regimes and refinement's effect is a delta over an unaltered causal estimate. Used as an auto-labeller, a fixed 3D detector trained on 25% human labels plus MotionSync pseudo-labels reaches 96.9% of its full-supervision mean average precision (mAP) on Waymo, and at a 10% budget the non-causal pass accounts for +3.3 mAP/L2 over pseudo-labels from the same tracker's causal stage. Re-fitting the online tracker on its own refined output recovers 73% of the benefit of human supervision, while its causal output is worse supervision than no re-fitting at all. As a tracker MotionSync is at parity with the leading published offline entries on the headline metric and ahead of them on error composition, which is where a refinement pass can act at all: it reduces misses and fragmentations together, the signature of gap completion rather than of a tuned detector.