we projected the 3D boxes which were predicted by locNet into their FV as the input. Table. \[table:det\] shows that our method outperformed them by a large margin.

### Generic Features for 3D Detection

We pre-trained MV3D and Frustum-PointNet on the $A^2$ dataset, and fine-tuned them on KITTI dataset for 3D car bounding box regression. Table. \[table:improve\_3ddet\] summarizes the 3D car detection performance on the KITTI dataset. The $A^2$ dataset pre-trained model yielded an additional increase in performance, revealing that the $A^2$ dataset can provide generic features for 3D object detection task.

[lcccc]{} Methods &

  ----------
   w/ $A^2$
  ----------

  : The AP of different methods for 3D car detection on KITTI dataset w/ or w/o $A^2$ Dataset. []{data-label="table:improve_3ddet"}

& Easy & Moderate & Hard\

MV3D & & 65.53 &58.97 & 59.14\
MV3D &$\surd$ &**68.56** &**60.35** & **60.99**\
F-pointnet-v1 & & 83.26 & 69.28 & 62.56\
F-pointnet-v1 & $\surd$ &**84.89** & **71.97** & **64.07**\
F-pointnet-v2 & & 83.76 & 70.92 & 63.65\
F-pointnet-v2 & $\surd$ &**85.11** &**72.13** & **64.24**\

Conclusions
===========

In this work, we reformulate the challenging molecular structure determination problem and propose a learning-based framework. The newly designed $A^2$-Net predicts accurate amino acid proposals with our APRoI layer and 