# CXTrack: Improving 3D Point Cloud Tracking with Contextual Information

arXiv: https://arxiv.org/abs/2211.08542 · HTML: https://mlsanity.com/paper/2211.08542/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 38 references that are in the map.
- First posted on arXiv 12 Nov 2022.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [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)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [An End-to-End Transformer Model for 3D Object Detection](https://mlsanity.com/paper/2109.08141/) (2021)
- [Voxel Transformer for 3D Object Detection](https://mlsanity.com/paper/2109.02497/) (2021)
- [Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking](https://mlsanity.com/paper/2103.11681/) (2021)
- [Group-Free 3D Object Detection via Transformers](https://mlsanity.com/paper/2104.00678/) (2021)
- [A Novel Performance Evaluation Methodology for Single-Target Trackers](https://mlsanity.com/paper/1503.01313/) (2015)
- [Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds](https://mlsanity.com/paper/2203.10314/) (2022)
- [P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds](https://mlsanity.com/paper/2005.13888/) (2020)
- [Leveraging Shape Completion for 3D Siamese Tracking](https://mlsanity.com/paper/1903.01784/) (2019)
- [Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2108.04728/) (2021)
- [PTTR: Relational 3D Point Cloud Object Tracking with Transformer](https://mlsanity.com/paper/2112.02857/) (2021)
- [Probabilistic 3D Multi-Object Tracking for Autonomous Driving](https://mlsanity.com/paper/2001.05673/) (2020)
- [PTT: Point-Track-Transformer Module for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2108.06455/) (2021)
- [Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2203.01730/) (2022)
- [3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds](https://mlsanity.com/paper/2111.04426/) (2021)
- [3D Object Tracking with Transformer](https://mlsanity.com/paper/2110.14921/) (2021)
- [3D-SiamRPN: An End-to-End Learning Method for Real-Time 3D Single Object Tracking Using Raw Point Cloud](https://mlsanity.com/paper/2108.05630/) (2021)

## Built on by (most cited first, 28 of 28)

- [MBPTrack: Improving 3D Point Cloud Tracking with Memory Networks and Box Priors](https://mlsanity.com/paper/2303.05071/) (2023)
- [DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks](https://mlsanity.com/paper/2304.00571/) (2023)
- [Towards Category Unification of 3D Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2401.11204/) (2024)
- [M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking](https://mlsanity.com/paper/2312.06117/) (2023)
- [P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds](https://mlsanity.com/paper/2407.05238/) (2024)
- [CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking](https://mlsanity.com/paper/2511.15580/) (2025)
- [Modeling Continuous Motion for 3D Point Cloud Object Tracking](https://mlsanity.com/paper/2303.07605/) (2023)
- [Robust 3D Tracking with Quality-Aware Shape Completion](https://mlsanity.com/paper/2312.10608/) (2023)
- [FocusTrack: One-Stage Focus-and-Suppress Framework for 3D Point Cloud Object Tracking](https://mlsanity.com/paper/2602.24133/) (2026)
- [SeqTrack3D: Exploring Sequence Information for Robust 3D Point Cloud Tracking](https://mlsanity.com/paper/2402.16249/) (2024)
- [VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking](https://mlsanity.com/paper/2408.02263/) (2024)
- [3D Single-object Tracking in Point Clouds with High Temporal Variation](https://mlsanity.com/paper/2408.02049/) (2024)
- [TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking](https://mlsanity.com/paper/2507.19908/) (2025)
- [Motion-to-Matching: A Mixed Paradigm for 3D Single Object Tracking](https://mlsanity.com/paper/2308.11875/) (2023)
- [BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View](https://mlsanity.com/paper/2309.02185/) (2023)
- [GSOT3D: Towards Generic 3D Single Object Tracking in the Wild](https://mlsanity.com/paper/2412.02129/) (2024)
- [Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds](https://mlsanity.com/paper/2507.03441/) (2025)
- [TFTrack: A Template-Free Framework for Efficient 3D Point Cloud Tracking](https://mlsanity.com/paper/2609.07738/) (2026)
- [Multi-Correlation Siamese Transformer Network with Dense Connection for 3D Single Object Tracking](https://mlsanity.com/paper/2312.11051/) (2023)
- [Small Object Tracking in LiDAR Point Cloud: Learning the Target-awareness Prototype and Fine-grained Search Region](https://mlsanity.com/paper/2401.13285/) (2024)
- [SiamMo: Siamese Motion-Centric 3D Object Tracking](https://mlsanity.com/paper/2408.01688/) (2024)
- [Spatio-Temporal Bi-directional Cross-frame Memory for Distractor Filtering Point Cloud Single Object Tracking](https://mlsanity.com/paper/2403.15831/) (2024)
- [EasyTrack: Efficient and Compact One-stream 3D Point Clouds Tracker](https://mlsanity.com/paper/2404.05960/) (2024)
- [Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions](https://mlsanity.com/paper/2501.07133/) (2025)
- [MambaTrack3D: A State Space Model Framework for LiDAR-Based Object Tracking under High Temporal Variation](https://mlsanity.com/paper/2511.15077/) (2025)
- [Temporally Consistent Long-Term Memory for 3D Single Object Tracking](https://mlsanity.com/paper/2604.13789/) (2026)
- [Leveraging Deep Learning for Object and Position Recognition of Load Carriers for Autonomous Logistics Vehicles](https://mlsanity.com/paper/2606.16042/) (2026)
- [SAVTrack: Selective Vote Aggregation for Reliability-Aware Point Cloud Tracking](https://mlsanity.com/paper/2609.16662/) (2026)

## Abstract (arXiv)

3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is crucial for effective object tracking. However, points containing such useful information are often overlooked and cropped out in existing methods, leading to insufficient use of important contextual knowledge. To address this issue, we propose CXTrack, a novel transformer-based network for 3D object tracking, which exploits ConteXtual information to improve the tracking results. Specifically, we design a target-centric transformer network that directly takes point features from two consecutive frames and the previous bounding box as input to explore contextual information and implicitly propagate target cues. To achieve accurate localization for objects of all sizes, we propose a transformer-based localization head with a novel center embedding module to distinguish the target from distractors. Extensive experiments on three large-scale datasets, KITTI, nuScenes and Waymo Open Dataset, show that CXTrack achieves state-of-the-art tracking performance while running at 34 FPS.