A Fast Hole-Filling Method for Triangular Mesh in Additive Repair

Feng, Chao; Liang, Jin; Maodong, Ren; Qiao, Gen; Wang, Lu; Liu, Shifan

doi:10.3390/app10030969

Cited by 19 publications

(18 citation statements)

References 37 publications

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“…This can include suppression of holes and pockets [21]. Also mesh-based hole filling and detection approach is discussed in [22]. Apparently, detection of holes in meshes is quite simple, because mesh nodes at the end are given as boundary seeds.…”

Section: Related Workmentioning

confidence: 99%

CAD integrated automatic recognition of weld paths

Tran

Lobov

Kaasa

et al. 2021

Int J Adv Manuf Technol

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In this paper, a CAD integrated method is proposed for automatic recognition of potential weld locations in large assembly structures predominantly comprised of weld joints. The intention is to reduce the total man-hours spent on manually locating, assigning, and maintaining weld-related information throughout the product life cycle. The method utilizes spatial analysis of extracted stereolithographic data in combination with available CAD functions to determine whether the accessibility surrounding a given intersection edge is sufficient for welding. To demonstrate the method, a system is developed in Siemens NX using their NXOpen Python API. The paper presents the application of the method to real-life use cases in varying complexity in cooperation with industrial partners. The system is able to correctly recognize almost all weld lines for the parts considered within a few minutes. Some exceptions are known for particular intersection lines located deep within notched joints and geometries weldable through sequential assembly, which are left as a subject to further works.

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Section: Related Workmentioning

confidence: 99%

CAD integrated automatic recognition of weld paths

Tran

Lobov

Kaasa

et al. 2021

Int J Adv Manuf Technol

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“…The identification of open holes is the first step in the filling process. In this paper, open holes are identified using a half-edge data structure, as described in the existing literature [19]. Specifically, adjacent triangles associated with a certain edge are identified from the triangular facets organized inside the half-edge data structure, as shown in Figure 3a.…”

Section: Identification and Classification Of Open Holesmentioning

confidence: 99%

A Parallel Method for Open Hole Filling in Large-Scale 3D Automatic Modeling Based on Oblique Photography

et al. 2021

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Common methods of filling open holes first reaggregate them into closed holes and then use a closed hole filling method to repair them. These methods have problems such as long calculation times, high memory consumption, and difficulties in filling large-area open holes. Hence, this paper proposes a parallel method for open hole filling in large-scale 3D automatic modeling. First, open holes are automatically identified and divided into two categories (internal and external). Second, the hierarchical relationships between the open holes are calculated in accordance with the adjacency relationships between partitioning cells, and the open holes are filled through propagation from the outer level to the inner level with topological closure and height projection transformation. Finally, the common boundaries between adjacent open holes are smoothed based on the Laplacian algorithm to achieve natural transitions between partitioning cells. Oblique photography data from an area of 28 km2 in Dongying, Shandong, were used for validation. The experimental results reveal the following: (i) Compared to the Han method, the proposed approach has a 12.4% higher filling success rate for internal open holes and increases the filling success rate for external open holes from 0% to 100%. (ii) Concerning filling efficiency, the Han method can achieve hole filling only in a small area, whereas with the proposed method, the size of the reconstruction area is not restricted. The time and memory consumption are improved by factors of approximately 4–5 and 7–21, respectively. (iii) In terms of filling accuracy, the two methods are basically the same.

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“…Further study in utilizing different mesh fixing techniques, depending on the mesh, would help reduce errors and make the system more robust and autonomous. Newer algorithms suggest that it may be possible to get better results by distinguishing between different hole sizes and performing the appropriate filling method as proposed in [ 34 ]. While hole size alone might not be enough for automatic mesh fixing, additional characteristics of the mesh and boundary may increase its efficacy.…”

Section: Future Workmentioning

confidence: 99%

DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients

Makhsous

Bharadwaj

Atkinson

et al. 2020

Sensors

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Diabetes is a global epidemic that impacts millions of people every year. Enhanced dietary assessment techniques are critical for maintaining a healthy life for a diabetic patient. Moreover, hospitals must monitor their diabetic patients’ food intake to prescribe a certain amount of insulin. Malnutrition significantly increases patient mortality, the duration of the hospital stay, and, ultimately, medical costs. Currently, hospitals are not fully equipped to measure and track a patient’s nutritional intake, and the existing solutions require an extensive user input, which introduces a lot of human errors causing endocrinologists to overlook the measurement. This paper presents DietSensor, a wearable three-dimensional (3D) measurement system, which uses an over the counter 3D camera to assist the hospital personnel with measuring a patient’s nutritional intake. The structured environment of the hospital provides the opportunity to have access to the total nutritional data of any meal prepared in the kitchen as a cloud database. DietSensor uses the 3D scans and correlates them with the hospital kitchen database to calculate the exact consumed nutrition by the patient. The system was tested on twelve volunteers with no prior background or familiarity with the system. The overall calculated nutrition from the DietSensor phone application was compared with the outputs from the 24-h dietary recall (24HR) web application and MyFitnessPal phone application. The average absolute error on the collected data was 73%, 51%, and 33% for the 24HR, MyFitnessPal, and DietSensor systems, respectively.

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A Fast Hole-Filling Method for Triangular Mesh in Additive Repair

Cited by 19 publications

References 37 publications

CAD integrated automatic recognition of weld paths

CAD integrated automatic recognition of weld paths

A Parallel Method for Open Hole Filling in Large-Scale 3D Automatic Modeling Based on Oblique Photography

DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients

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