Version 1
: Received: 6 August 2017 / Approved: 7 August 2017 / Online: 7 August 2017 (11:03:23 CEST)
How to cite:
Zhang, C.; Hu, Y. CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid. Preprints2017, 2017080022. https://doi.org/10.20944/preprints201708.0022.v1
Zhang, C.; Hu, Y. CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid. Preprints 2017, 2017080022. https://doi.org/10.20944/preprints201708.0022.v1
Zhang, C.; Hu, Y. CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid. Preprints2017, 2017080022. https://doi.org/10.20944/preprints201708.0022.v1
APA Style
Zhang, C., & Hu, Y. (2017). CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid. Preprints. https://doi.org/10.20944/preprints201708.0022.v1
Chicago/Turabian Style
Zhang, C. and Yu Hu. 2017 "CuFusion: Accurate Real‐time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid" Preprints. https://doi.org/10.20944/preprints201708.0022.v1
Abstract
Given a stream of depth images with a known cuboid reference object present in the scene, we propose a novel approach for accurate camera tracking and volumetric surface reconstruction in real-time. Our contribution in this paper is threefold: (a) utilizing a priori knowledge of the cuboid reference object, we keep drift-free camera tracking without explicit global optimization; (b) we improve the fineness of the volumetric surface representation by proposing a prediction-corrected data fusion strategy rather than simple moving average, which enables accurate reconstruction of high-frequency details such as sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D containing both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for quantitative evaluation of 3D reconstruction algorithms. We test our algorithm on our dataset and demonstrate its accuracy compared with other state-of-the-art algorithms. We release both our dataset and code as opensource1 for other researchers to reproduce and verify our results.
Keywords
real‐time reconstruction; SLAM; kinect sensors; depth cameras; open source
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.
The commenter has declared there is no conflict of interests.
Comment:
Sorry for that incompleteness... I've added the links as footnote 1 in the text. However some error happened when we preprint the manuscript online. I'm now contacting the editors of preprints.org and submitting a follow-up version to fix this issue.
Thank you so much for reminding us of such a carelessness!!!
The commenter has declared there is no conflict of interests.
Comment:
For quick access of the code and data before our re-submission getting online, please checkout this code repo, and see the instructions in README.MD: https://github.com/zhangxaochen/CuFusion
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Commenter: Bassam Arrfou
The commenter has declared there is no conflict of interests.
Commenter:
The commenter has declared there is no conflict of interests.
Thank you so much for reminding us of such a carelessness!!!
Commenter:
The commenter has declared there is no conflict of interests.
https://github.com/zhangxaochen/CuFusion