Papers › 4D-Net for Learned Multi-Modal Alignment

4D-Net for Learned Multi-Modal Alignment

2 Sep 2021ICCV 2021 10arXiv:2109.01066archive 2025-07-28

AJ Piergiovanni, Vincent Casser, Michael S. Ryoo, Anelia Angelova

We present 4D-Net, a 3D object detection approach, which utilizes 3D Point Cloud and RGB sensing information, both in time. We are able to incorporate the 4D information by performing a novel dynamic connection learning across various feature representations and levels of abstraction, as well as by observing geometric constraints. Our approach outperforms the state-of-the-art and strong baselines on the Waymo Open Dataset. 4D-Net is better able to use motion cues and dense image information to detect distant objects more successfully.

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