0.44
  3D-ResNet+RoIpool8(Raw volume)   0.610          0.55
  $A^2$-Net (APRoI8)               0.711          0.67
  $A^2$-Net w/o Neighbor Loss      0.865          0.72
  $A^2$-Net                        **0.891**    **0.91**
  -------------------------------- ----------- ----------

  : The results of detection and threading comparing with other 3D object detection methods. []{data-label="table:det"}

  --------------- ---------- ---------
  Methods         Coverage     RMSD
  DFS$_o$         0.65          3.5
  DFS$_d$         0.68          3.1
  DFS$_d$+PBNet   0.89          2.6
  MCTS            0.72          2.9
  MCTS+PBNet      **0.91**    **2.0**
  --------------- ---------- ---------

  : The results of threading by DFS-based methods and the proposed MCTS+PBNet.[]{data-label="table:threading"}

We first directly applied 3D VGG with RoI pooling for amino acid detection, which only achieves 0.36 mAP, while 3D ResNet-10 [@he2015deep] achieves 0.416 mAP. The last $conv$ feature volumes were used for 3D region proposal. By analyzing the intermediate output, we found that the network seemed to be dominated by the 3D region proposal task, which made the last $conv$ feature volumes to be only sensitive to the existence of the amino acid. There was little category-specific information left in the feature volumes. So we directly performed RoI pooling on the raw input cube and trained the recNet, which achieved 0.610 mAP. This verified our assumption that the category-specific information may be discarded in the feature map of the locNet model.

We then replaced the RoI pooling 