|
|
| CARVIEW |
All tasks take a sequence or a single LiDAR sweep as input. SSC aims to densify, complete, and semantically predict on the t=0 frame. Point/occupancy forecasting outputs a sparse and Lagrangian specification of the scene geometry's motion field. OCF combines scene completion and occupancy forecasting in a spatial-temporal way, outputting a dense and Eulerian motion field.
Experiment Results
Performance w.r.t. temporal ranges. The per-frame IoU is shown for each method when forecasting 10 future frames with 5/10 input frames.
Qualitative Result. The ture positive and false negative are shown in green and red respectively.
BibTeX
@article{Liu2023occ4cast,
title={LiDAR-based 4D Occupancy Completion and Forecasting},
author={Xinhao Liu and Moonjun Gong and Qi Fang and Haoyu Xie and Yiming Li and Hang Zhao and Chen Feng},
journal={arXiv preprint arXiv:2310.11239},
year={2023}
}