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Sensors 2017, 17(10), 2260;

CuFusion: Accurate Real-Time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid

College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
Author to whom correspondence should be addressed.
Received: 6 August 2017 / Revised: 19 September 2017 / Accepted: 27 September 2017 / Published: 1 October 2017
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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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 precisely manufactured 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 a simple moving average, which enables accurate reconstruction of high-frequency details such as the sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D that contains both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for the 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 open-source ( for other researchers to reproduce and verify our results. View Full-Text
Keywords: real-time reconstruction; SLAM; Kinect sensors; depth; cameras; open source real-time reconstruction; SLAM; Kinect sensors; depth; cameras; open source

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Zhang, C.; Hu, Y. CuFusion: Accurate Real-Time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid. Sensors 2017, 17, 2260.

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