from a Region of Interest (RoI) and pooled to the same size before predicting their categories and coordinates. RoI pooling, RoI Warping and RoIAlign  [@girshick2014rich; @dai2016instance; @mask-rcnn] are popular techniques for RoI extraction, all of which break the original aspect ratio of the objects to account for their variations in natural images filmed with different angles and distances. However, in some scenarios like in our work, aspect ratios of different types of amino acids should be preserved. To tackle this problem, we proposed an Aspect-Ratio Preserved RoI (APRoI) layer to capture the aspect ratio in amino acids.

3D object detection plays a key role in a variety of real-world applications, such as autonomous driving [@gomez2016pl], augmented/virtual reality and identification of disease diagnosis. MV3D [@cvpr17chen] focuses on very sparse data (LiDAR point cloud) and projects 3D data into 2D multi-view images. It is less effective in the amino acid detection task where difficulties may come from dense objects and the ambiguity in overlapping regions. VoxelNet [@zhou2017voxelnet] groups very sparse points for encoding voxel features to model point interactions. Frustum-PointNet [@qi2017frustum] extracts points within the frustum from 2D box to form a frustum point cloud, which may generate too much noise, especially 