[R] DFA3D: 3D Deformable Attention For 2D-to-3D Feature Lifting

We introduce a new operator, called **3D D**e**F**ormable **A**ttention (**DFA3D**), for 2D-to-3D feature lifting, which transforms multi-view 2D image features into a unified 3D space for 3D object detection.

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[Comparisons of feature lifting methods.](https://preview.redd.it/3frplpkgqkkb1.png?width=2382&format=png&auto=webp&s=0386f87d5363b56cb9d992e8b74070a45eefd3bf)

Existing feature lifting approaches, such as Lift-Splat-based and 2D attention-based, either use estimated depth to get pseudo LiDAR features and then splat them to a 3D space, which is a one-pass operation without feature refinement, or ignore depth and lift features by 2D attention mechanisms, …

2d image 2d-to-3d 3d object detection attention detection feature features image lidar machinelearning space