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 opensource 1 for other researchers to reproduce and verify our results.
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.
Recently, Christian Cortés García proposed and studied a continuous modified Leslie–Gower model with harvesting and alternative food for predator and Holling-II functional response, and proved that the model undergoes transcritical bifurcation, saddle-node bifurcation and Hopf bifurcation. In this paper, we dedicate ourselves to investigating the bifurcation problems of the discrete version of the model by using the Center Manifold Theorem and bifurcation theory, and obtain sufficient conditions for the occurrences of the transcritical bifurcation and Neimark–Sacker bifurcation, and the stability of the closed orbits bifurcated. Our numerical simulations not only illustrate corresponding theoretical results, but also reveal new dynamic chaos occurring, which is an essential difference between the continuous system and its corresponding discrete version.
The 3D object reconstruction from depth image streams using Kinect-style depth cameras has been extensively studied. In this paper, we propose an approach for accurate camera tracking and volumetric dense surface reconstruction, assuming that a known cuboid reference object is present in the scene. Our contribution is threefold. First, we maintain the drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process. Second, we reformulate the problem of depth stream fusion as a binary classification problem, enabling highfidelity surface reconstruction, especially in the concave zones of objects. Third, we further present a surface denoising strategy to mitigate the topological inconsistency (e.g., holes and dangling triangles), which facilitates the generation of a noise-free triangle mesh. We extend our public dataset CU3D with several new image sequences, test our algorithm on these sequences, and quantitatively compare them with other state-of-the-art algorithms. Both our dataset and our algorithm are available as open-source content at https://github.com/zhangxaochen/CuFusion for other researchers to reproduce and verify our results. INDEX TERMS 3D object reconstruction, depth cameras, Kinect sensors, open source, signal denoising, SLAM. CHEN ZHANG received the B.S. degree in computer science from Zhejiang University, China, in 2013, and the Ph.D. degree from the College of Computer Science and Technology, Zhejiang University, China. He is currently with the State Key Laboratory of CAD and CG, Computer Animation and Perception Group, Zhejiang University. His primary research interests include simultaneous localization and mapping (SLAM), and 3D reconstruction.
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.
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