in a dense object detection task. In this work, we designed a pure 3D detector.

### 3D Pose Estimation

Given well labeled 3D joint locations, 3D pose estimation aims to determine the precise joint locations in 3D space. For instance, 3D human pose estimation attempts to regress 16 key points in the human body based on 3D joint locations of a human skeleton from 2D images [@pose_Zhou_2017_ICCV; @pose_martinez_2017_3dbaseline]. However, the existing 3D pose estimation are based on 2D images. In this work, we introduce a pure 3D pose estimation module to produce atomic coordinates in volumetric data.

Inspired by these breakthroughs, we designed a multi-task 3D neural network that first detects amino acids and then estimates the atom positions. We also took advantage of the sequence information and introduced a sequence-guided neighbor loss to train the network, which has not been explored before. In addition, we added a postprocessing with superior tree searching algorithm – MCTS for main chain threading. We believe that this is the first attempt using MCTS to trace the boxes of amino acids guided by sequence.

Problem Definition
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Given a 3D Cryo-EM density volume and its protein sequence as inputs, our goal is to detect 