# Registration between Point Cloud Streams and Sequential Bounding Boxes via Gradient Descent

arXiv: https://arxiv.org/abs/2409.09312 · HTML: https://mlsanity.com/paper/2409.09312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 14 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [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)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)
- [Offboard 3D Object Detection from Point Cloud Sequences](https://mlsanity.com/paper/2103.05073/) (2021)
- [Focal Sparse Convolutional Networks for 3D Object Detection](https://mlsanity.com/paper/2204.12463/) (2022)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Auto4D: Learning to Label 4D Objects from Sequential Point Clouds](https://mlsanity.com/paper/2101.06586/) (2021)
- [Density Adaptive Point Set Registration](https://mlsanity.com/paper/1804.01495/) (2018)
- [Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking](https://mlsanity.com/paper/2004.09305/) (2020)
- [Efficient and accurate object detection with simultaneous classification and tracking](https://mlsanity.com/paper/2007.02065/) (2020)
- [Real-time 3D object proposal generation and classification under limited processing resources](https://mlsanity.com/paper/2003.10670/) (2020)
- [Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenes](https://mlsanity.com/paper/1901.08373/) (2019)
- [Speeding Up Iterative Closest Point Using Stochastic Gradient Descent](https://mlsanity.com/paper/1907.09133/) (2019)

## Abstract (arXiv)

In this paper, we propose an algorithm for registering sequential bounding boxes with point cloud streams. Unlike popular point cloud registration techniques, the alignment of the point cloud and the bounding box can rely on the properties of the bounding box, such as size, shape, and temporal information, which provides substantial support and performance gains. Motivated by this, we propose a new approach to tackle this problem. Specifically, we model the registration process through an overall objective function that includes the final goal and all constraints. We then optimize the function using gradient descent. Our experiments show that the proposed method performs remarkably well with a 40% improvement in IoU and demonstrates more robust registration between point cloud streams and sequential bounding boxes