# Efficient Bird Eye View Proposals for 3D Siamese Tracking

arXiv: https://arxiv.org/abs/1903.10168 · HTML: https://mlsanity.com/paper/1903.10168/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 18 of 18)

- [Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds](https://mlsanity.com/paper/2108.04728/) (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-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)
- [A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds](https://mlsanity.com/paper/2203.04232/) (2022)
- [Synchronize Feature Extracting and Matching: A Single Branch Framework for 3D Object Tracking](https://mlsanity.com/paper/2308.12549/) (2023)
- [Exploiting More Information in Sparse Point Cloud for 3D Single Object Tracking](https://mlsanity.com/paper/2210.00519/) (2022)
- [An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2303.12535/) (2023)
- [Correlation Pyramid Network for 3D Single Object Tracking](https://mlsanity.com/paper/2305.09195/) (2023)
- [STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking](https://mlsanity.com/paper/2306.17440/) (2023)
- [Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild](https://mlsanity.com/paper/2111.12728/) (2021)
- [Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking](https://mlsanity.com/paper/2209.00522/) (2022)
- [OST: Efficient One-stream Network for 3D Single Object Tracking in Point Clouds](https://mlsanity.com/paper/2210.08518/) (2022)
- [3D Single-object Tracking in Point Clouds with High Temporal Variation](https://mlsanity.com/paper/2408.02049/) (2024)
- [VPIT: Real-time Embedded Single Object 3D Tracking Using Voxel Pseudo Images](https://mlsanity.com/paper/2206.02619/) (2022)
- [MixCycle: Mixup Assisted Semi-Supervised 3D Single Object Tracking with Cycle Consistency](https://mlsanity.com/paper/2303.09219/) (2023)
- [BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View](https://mlsanity.com/paper/2309.02185/) (2023)
- [Visual Object Tracking across Diverse Data Modalities: A Review](https://mlsanity.com/paper/2412.09991/) (2024)
- [Variational Voxel Pseudo Image Tracking](https://mlsanity.com/paper/2302.05914/) (2023)

## Abstract (arXiv)

Tracking vehicles in LIDAR point clouds is a challenging task due to the sparsity of the data and the dense search space. The lack of structure in point clouds impedes the use of convolution filters usually employed in 2D object tracking. In addition, structuring point clouds is cumbersome and implies losing fine-grained information. As a result, generating proposals in 3D space is expensive and inefficient. In this paper, we leverage the dense and structured Bird Eye View (BEV) representation of LIDAR point clouds to efficiently search for objects of interest. We use an efficient Region Proposal Network and generate a small number of object proposals in 3D. Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network. We regularize the latter 3D Siamese network for shape completion to enhance its discrimination capability. Our method attempts to solve both for an efficient search space in the BEV space and a meaningful selection using 3D LIDAR point cloud. We show that the Region Proposal in the BEV outperforms Bayesian methods such as Kalman and Particle Filters in providing proposal by a significant margin and that such candidates are suitable for the 3D Siamese network. By training our method end-to-end, we outperform the previous baseline in vehicle tracking by 12% / 18% in Success and Precision when using only 16 candidates.