Real-time 3D Reconstruction Using a Combination of Point-Based and Volumetric Fusion

摘要

Real-time 3D reconstruction using low-cost commodity sensors like Kinect or Xtion has been successfully applied in a wide range of fields like augmented reality, robotic teleoperation, and medical diagnosis. Due to the assumption of static scene, popular 3D reconstruction technologies such as KinectFusion and KinFu, find truthful reconstruction with fast motion camera or segmenting a moving object to be a challenge. In this paper, we propose a weighted iterative closest point (ICP) algorithm that uses both depth and RGB information to enhance the stability of camera tracking. Additionally, a GPU-based region growing method that combines depth, normal and intensity level as similarity criteria, is also applied to segment foreground moving objects accurately. For real-time processing and GPU memory efficiency, we also design a combination of point-based and volumetric representation to reconstruct moving objects and static scene, respectively. Both qualitative and quantitative results show that our proposed method improves real-time 3D reconstruction on the performance of camera tracking and segmentation of moving objects with reduced computational complexity.