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Jul, 2023
DFA3D:2D转3D特征提升的3D可变形注意力
DFA3D: 3D Deformable Attention For 2D-to-3D Feature Lifting
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Hongyang Li, Hao Zhang, Zhaoyang Zeng, Shilong Liu, Feng Li...
TL;DR
通过提出一种新的运算符,即3D可变形注意力(DFA3D),将多视图2D图像特征转换成统一的3D空间,用于3D物体检测,并通过深度数据和转换器结构逐层提炼特征,解决深度模糊问题,实现了在nuScenes数据集上平均+1.41% mAP的一致改进和高达+15.1% mAP改进的实验证明。
Abstract
In this paper, we propose a new operator, called
3d deformable attention
(
dfa3d
), for
2d-to-3d feature lifting
, which transforms multi-vie
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