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Efficiently detect 3D objects in 2D range image with graph convolution kernels

Efficiently detect 3D objects in 2D range image with graph convolution kernels


To the Point: Efficient 3D Object Detection in the Range Image with Graph Convolution Kernels

arXiv paper abstract https://arxiv.org/abs/2106.13381



3D object detection is vital for many robotics applications.

For tasks where a 2D perspective range image exists, we propose to learn a 3D representation directly from this range image view.


... performs competitively on the Waymo Open Dataset and improves the state-of-the-art AP for pedestrian detection from 69.7% to 75.5%.


... our smallest model, which still outperforms the popular PointPillars in quality, requires 180 times fewer FLOPS and model parameters


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