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

arXiv 2409.09312 ↗First posted 14 Sep 2024cs.CV cs.ROcited by 0

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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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 14 Sep 2024.

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15 of 27 references.

References · 15

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  5. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  6. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144
  7. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  8. 2022Vision-Centric BEV Perception: A Surveycited by 126
  9. 2021Auto4D: Learning to Label 4D Objects from Sequential Point Cloudscited by 42
  10. 2018Density Adaptive Point Set Registrationcited by 11
  11. 2020Joint Spatial-Temporal Optimization for Stereo 3D Object Trackingcited by 7
  12. 2020Efficient and accurate object detection with simultaneous classification and trackingcited by 6
3 more
  1. 2020Real-time 3D object proposal generation and classification under limited processing resourcescited by 5
  2. 2019Three-dimensional Backbone Network for 3D Object Detection in Traffic Scenescited by 3
  3. 2019Speeding Up Iterative Closest Point Using Stochastic Gradient Descentcited by 3

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