Subject: Keywords: 3D object reconstruction, depth cameras, Kinect sensors; open source, signal denoising, SLAM
Online: 9 April 2019 (12:24:34 CEST)
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 a known cuboid reference object is present in the scene. Our contribu¬tion is three-fold. (a) We maintain drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process. (b) We reformulate the problem of depth stream fusion as a binary classification problem, enabling high-fidelity surface reconstruction, especially in the con¬cave zones of objects. (c) 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 oth-er researchers to reproduce and verify our results.
ARTICLE | doi:10.20944/preprints201708.0022.v1
Subject: Mathematics & Computer Science, Other Keywords: real‐time reconstruction; SLAM; kinect sensors; depth cameras; open source
Online: 7 August 2017 (11:03:23 CEST)
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.
ARTICLE | doi:10.20944/preprints202007.0669.v1
Subject: Keywords: volt-ampere characteristics; battery mathematical model; mechanism function; fuel cell
Online: 28 July 2020 (09:39:10 CEST)
The corrected mechanism model of battery voltammetric function is helpful to guide the development and application of battery. There are two scientific issues that need to be answered: First, how many mechanisms do batteries have; Second, how to establish the mechanism model separately under the overlapping of these mechanisms. Volt-ampere characteristics of both linear state and nonlinear state exist; the monotonic decreasing of volt-ampere characteristics indicates that the battery have only three kinds of mechanisms. Without changing the basic form of the function and under the principle of the mechanism function’s working region considered, we propose a mechanism function which satisfies the monotonic decreasing characteristic of the voltammetric curve of battery, via the derivative law of each mechanism function in the voltammetric function of battery. By using the voltammetric data, the obtained cell mechanism function can accurately predict the potential (current or voltage) when the independent variable of the cell is zero, and provide the theoretical basis for the internal working mechanism of the cell, which can guide the practice.
Subject: Life Sciences, Biochemistry Keywords: digestible energy; growth performance; microbiome; metabolome; donkey
Online: 1 March 2021 (13:28:35 CET)
Little information is available regarding the impacts of dietary energy level on the gut microbiota and metabolites of donkeys. This studied aimed to explore the effects of dietary energy content on growth performance, intestinal microbiome and metabolome of Dezhou donkeys. Thirty-six 9-month-old male Dezhou donkeys were assigned to two groups fed low or high content energy diets (LE or HE). Results showed that donkeys fed HE had improved (P < 0.05) the average daily gain (ADG) and feed efficiency (G/F), compared with those receiving LE diet. Compared to the LE group, feeding HE specially increased the abundances of unidentified_Prevotellaceae (P = 0.02) while decreased the richness of unidentified_Ruminococcaceae (P = 0.05) of donkeys. Compared to LE group, feeding HE diet significantly (P < 0.05) affected the metabolic pathways involving the aspartate metabolism and urea cycle. In addition, the increased bacteria and metabolites in the HE-fed group exhibited a positive correlation with improved growth performance of donkeys. Taken together, feeding HE diet increased the richness of some specific bacteria and upregulated growth-related metabolic pathways, which contributed to the augmented growth performance of donkeys. Thus, it is a recommendable dietary strategy to feed HE diet to fattening donkey for superior production performance and feed efficiency.