# OSP2B: One-Stage Point-to-Box Network for 3D Siamese Tracking

arXiv: https://arxiv.org/abs/2304.11584 · HTML: https://mlsanity.com/paper/2304.11584/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 23 Apr 2023.

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

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Deep Hough Voting for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1904.09664/) (2019)
- [Fully-Convolutional Siamese Networks for Object Tracking](https://mlsanity.com/paper/1606.09549/) (2016)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Ocean: Object-aware Anchor-free Tracking](https://mlsanity.com/paper/2006.10721/) (2020)
- [SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking](https://mlsanity.com/paper/1911.07241/) (2019)
- [ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond](https://mlsanity.com/paper/2202.10108/) (2022)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [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)
- [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)
- [Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook](https://mlsanity.com/paper/2112.02838/) (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)
- [3D Siamese Transformer Network for Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2207.11995/) (2022)
- [VSA: Learning Varied-Size Window Attention in Vision Transformers](https://mlsanity.com/paper/2204.08446/) (2022)
- [Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters](https://mlsanity.com/paper/1811.10863/) (2018)
- [GLT-T: Global-Local Transformer Voting for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2211.10927/) (2022)
- [Learning Localization-aware Target Confidence for Siamese Visual Tracking](https://mlsanity.com/paper/2204.14093/) (2022)

## Built on by (most cited first, 16 of 16)

- [Towards Category Unification of 3D Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2401.11204/) (2024)
- [P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds](https://mlsanity.com/paper/2407.05238/) (2024)
- [MVCTrack: Boosting 3D Point Cloud Tracking via Multimodal-Guided Virtual Cues](https://mlsanity.com/paper/2412.02734/) (2024)
- [CompTrack: Information Bottleneck-Guided Low-Rank Dynamic Token Compression for Point Cloud Tracking](https://mlsanity.com/paper/2511.15580/) (2025)
- [FocusTrack: One-Stage Focus-and-Suppress Framework for 3D Point Cloud Object Tracking](https://mlsanity.com/paper/2602.24133/) (2026)
- [VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking](https://mlsanity.com/paper/2408.02263/) (2024)
- [TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking](https://mlsanity.com/paper/2507.19908/) (2025)
- [BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View](https://mlsanity.com/paper/2309.02185/) (2023)
- [InScope: A New Real-world 3D Infrastructure-side Collaborative Perception Dataset for Open Traffic Scenarios](https://mlsanity.com/paper/2407.21581/) (2024)
- [Visual Object Tracking across Diverse Data Modalities: A Review](https://mlsanity.com/paper/2412.09991/) (2024)
- [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)
- [SiamMo: Siamese Motion-Centric 3D Object Tracking](https://mlsanity.com/paper/2408.01688/) (2024)
- [Robust Single Object Tracking in LiDAR Point Clouds under Adverse Weather Conditions](https://mlsanity.com/paper/2501.07133/) (2025)
- [A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life](https://mlsanity.com/paper/2603.22323/) (2026)
- [SAVTrack: Selective Vote Aggregation for Reliability-Aware Point Cloud Tracking](https://mlsanity.com/paper/2609.16662/) (2026)

## Abstract (arXiv)

Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-wise scores. However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. Towards these concerns, we propose a simple yet effective one-stage point-to-box network for point cloud-based 3D single object tracking. It synchronizes 3D proposal generation and center-ness score prediction by a parallel predictor without tedious hyper-parameters. To guide a task-aligned score ranking of proposals, a center-aware focal loss is proposed to supervise the training of the center-ness branch, which enhances the network's discriminative ability to distinguish proposals of different quality. Besides, we design a binary target classifier to identify target-relevant points. By integrating the derived classification scores with the center-ness scores, the resulting network can effectively suppress interference proposals and further mitigate task misalignment. Finally, we present a novel one-stage Siamese tracker OSP2B equipped with the designed network. Extensive experiments on challenging benchmarks including KITTI and Waymo SOT Dataset show that our OSP2B achieves leading performance with a considerable real-time speed.Code will be available at https://github.com/haooozi/OSP2B.