RECAP: 3D Traffic Reconstruction

On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.

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Publication Details

Published
2024-12-04
DOI
https://doi.org/10.1145/3636534.3690691
Citations
3
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
0.62

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article

RECAP: 3D Traffic Reconstruction

Ramesh Govindan, Weiwu Pang, Jeongyeup Paek, Christina Suyong Shin et al.
3 citations
Remote Sensing and LiDAR Applications
0.62
article

RECAP: 3D Traffic Reconstruction

Ramesh Govindan, Weiwu Pang, Jeongyeup Paek, Christina Suyong Shin, Fan Bai, Chuan Li, Fawad Ahmad
article en
3 citations

Abstract

On-vehicle 3D sensing technologies, such as LiDARs and stereo cameras, enable a novel capability, 3D traffic reconstruction. This produces a volumetric video consisting of a sequence of 3D frames capturing the time evolution of road traffic. 3D traffic reconstruction can help trained investigators reconstruct the scene of an accident. In this paper, we describe the design and implementation of RECAP, a system that continuously and opportunistically produces 3D traffic reconstructions from multiple vehicles. RECAP builds upon prior work on point cloud registration, but adapts it to settings with minimal point cloud overlap (both in the spatial and temporal sense) and develops techniques to minimize error and computation time in multi-way registration. On-road experiments and trace-driven simulations show that RECAP can, within minutes, generate highly accurate reconstructions that have 2× or more lower errors than competing approaches.

University of Southern California (US), General Motors (United States) (US), Rochester Institute of Technology (US), Chung-Ang University (KR)
National Science Foundation
Openalex Percentile: Top 34%
Remote Sensing and LiDAR Applications
0.62
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