4D-Net for Learned Multi-Modal Alignment

• URL: http://arxiv.org/abs/2109.01066v1
• Date: Thu, 2 Sep 2021 16:35:00 GMT
• Title: 4D-Net for Learned Multi-Modal Alignment
• Authors: AJ Piergiovanni and Vincent Casser and Michael S. Ryoo and Anelia Angelova
• Abstract summary: 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 connection learning across various feature representations and levels of abstraction, as well as by observing geometric constraints.
• Score: 87.58354992455891
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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