Version 1
: Received: 12 December 2018 / Approved: 13 December 2018 / Online: 13 December 2018 (09:41:36 CET)
Version 2
: Received: 8 April 2019 / Approved: 9 April 2019 / Online: 9 April 2019 (12:24:34 CEST)
How to cite:
ZHANG, C. CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras. Preprints2018, 2018120165. https://doi.org/10.20944/preprints201812.0165.v1
ZHANG, C. CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras. Preprints 2018, 2018120165. https://doi.org/10.20944/preprints201812.0165.v1
ZHANG, C. CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras. Preprints2018, 2018120165. https://doi.org/10.20944/preprints201812.0165.v1
APA Style
ZHANG, C. (2018). CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras. Preprints. https://doi.org/10.20944/preprints201812.0165.v1
Chicago/Turabian Style
ZHANG, C. 2018 "CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras" Preprints. https://doi.org/10.20944/preprints201812.0165.v1
Abstract
3D object reconstruction from depth image streams using Kinect-style depth cameras have been extensively studied. We propose an approach for accurate camera tracking and volumetric dense surface reconstruction, assuming a known cuboid reference object is present in the scene. Our contribution is threefold: (a) we keep drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process; (b) on the problem of depth stream fusion, we reformulate it as a binary classification problem, enabling high fidelity of surface reconstruction, especially in concave zones of the objects; (c) we further present a surface denoising strategy, facilitating the generation of noise-free triangle mesh, making the models more suitable for 3D printing and other applications. We extend our public dataset CU3D with several fresh image sequences, test our algorithm on these sequences and compare them with other state-of-the-art algorithms. Both our dataset and algorithm are available as open-source at https://github.com/zhangxaochen/CuFusion, for other researchers to reproduce and verify our results.
Keywords
3D object reconstruction, depth cameras, Kinect sensors; open source, signal denoising, SLAM
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
