A5010411472- 3D Surveying and Cultural Heritage
- 3D Shape Modeling and Analysis
- Remote Sensing and LiDAR Applications
- Fluid Dynamics and Turbulent Flows
- Optical measurement and interference techniques
- Rheology and Fluid Dynamics Studies
- Virtual Reality Applications and Impacts
- BIM and Construction Integration
- Fluid Dynamics and Thin Films
- Nanowire Synthesis and Applications
- Occupational Health and Safety Research
- Fluid Dynamics and Vibration Analysis
- Human Pose and Action Recognition
- Augmented Reality Applications
- Advancements in Semiconductor Devices and Circuit Design
- Video Surveillance and Tracking Methods
- Silicon Nanostructures and Photoluminescence
RMIT University
2019-2023
Victoria University
2023
MIT University
2022
Frictional pressure drop has been grasping the attention of many industrial applications associated with multi-phase and academia. Alongside United Nations, 2030 Agenda for Sustainable Development calls exigency giving to economic growth, a considerable reduction in power consumption is necessary co-up this vision adhere energy-efficient practices. Thereinto, drag-reducing polymers (DRPs), which do not require additional infrastructure, are much better option increasing energy efficiency...
Point clouds produced by either 3D scanners or multi-view images are often imperfect and contain noise outliers. This paper presents an end-to-end robust spherical harmonics approach to classifying objects. The proposed framework first uses the voxel grid of concentric spheres learn features over unit ball. We then limit order level suppress effect In addition, entire classification operation is performed in Fourier domain. As a result, our model learned that less sensitive data...
Three-dimensional point clouds produced by 3D scanners are often noisy and contain outliers. Such data inaccuracies can significantly affect current deep learning-based methods reduce their ability to classify objects. Most neural networks-based object classification were targeted achieve high accuracy without considering robustness. Thus, despite great success, they still fail good with low levels of noise This work is carried out develop a robust network structure that solidly identify The...
Virtual reality devices are designed to cover our vision so we unaware of surroundings and completely emerged into a different world. This limits VR user's ability move freely in crowded areas, such as classrooms, which the user interactions VR. We present framework that enables users see other people around them inside environment with help an external 3D depth camera. The proposed multi-VR use one tracking camera provides cost-effective solution. can also performing collaborative activities.
Classification of 3D shapes into physically meaningful categories is one the most important tasks in understanding immediate environment. Methods that leverage recent advancements deep learning have shown to outperform traditional approaches. However, performances those methods only been analyzed with relatively clean data. Three-dimensional measurement sets (point clouds) produced by scanners are rarely accurate and often contain noise, outliers or missing points. This paper presents an...
The task of learning from point cloud data is always challenging due to the often occurrence noise and outliers in data. Such inaccuracies can significantly influence performance state art deep networks their ability classify or segment objects. While there are some robust approaches, they computationally too expensive for real-time applications. This paper proposes a solution that includes novel pooling layers which greatly enhances network robustness perform faster than state-of-the-art...
The task of learning from point cloud data is always challenging due to the often occurrence noise and outliers in data. Such inaccuracies can significantly influence performance state-of-the-art deep networks their ability classify or segment objects. While there are some robust deep-learning approaches, they computationally too expensive for real-time applications. This paper proposes a solution that includes novel pooling layers which greatly enhance network robustness perform faster than...
Abstract Frictional pressure drop has been grasping the attention of many industrial applications associated with multi-phase and academia. Alongside United Nations, 2030 Agenda for Sustainable Development calls exigency giving to economic growth, a considerable reduction in power consumption is necessary co-up this vision adhere energy-efficient practices. Thereinto, drag-reducing polymers (DRPs), which do not require additional infrastructure, are much better option increasing energy...
We present a framework for the visualization of mechanical stress using augmented reality (AR) Thermoelastic Stress Analysis (TSA). The 2D images generated by TSA are converted to 3D map computer vision technology and then superimposed on real object AR. Our enables in-situ in geometrically complex structural components, which can assist design, manufacture, test, through-life sustainment failure-critical engineering assets. also discuss challenges such TSA-AR combination case study that...
In this paper, we present a robust spherical harmonics approach for the classification of point cloud-based objects. Spherical have been used over years, with several frameworks existing in literature. These approaches use variety based descriptors to classify We first investigated these robustness against data augmentation, such as outliers and noise, it has not studied before. Then propose convolution neural network framework object classification. The proposed uses voxel grid concentric...
The task of learning from point cloud data is always challenging due to the often occurrence noise and outliers in data. Such inaccuracies can significantly influence performance state-of-the-art deep networks their ability classify or segment objects. While there are some robust approaches, they computationally too expensive for real-time applications. This paper proposes a solution that includes novel pooling layer which greatly enhances network robustness performs faster than approaches....