A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh

Long, Hoang Thanh; Lee, Suk-Hwan; Kwon, Oh-Heum; Kwon, Ki-Ryong

doi:10.3390/electronics8101196

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…Low quality : In addition, such data typically contain noise, missing data, and resolution issues. Figure 1 shows 3D data representations of the Stanford Bunny [ 33 ] dataset with point cloud, voxel, and mesh data representations [ 34 ].…”

Section: 3d Data Representationsmentioning

confidence: 99%

“… The 3D data representations of the Stanford Bunny [ 33 ] model: point cloud ( left ), voxels ( middle ), and 3D mesh ( right ) [ 34 ]. …”

Section: Figurementioning

confidence: 99%

“…This is why voxel-based representations are unsuitable for high-resolution data representation. [33] model: point cloud (left), voxels (middle), and 3D mesh (right) [34].…”

Section: Voxelsmentioning

confidence: 99%

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Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Vinodkumar,

Karabulut,

Avots

et al. 2024

Entropy

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The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future.

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Section: 3d Data Representationsmentioning

confidence: 99%

“… The 3D data representations of the Stanford Bunny [ 33 ] model: point cloud ( left ), voxels ( middle ), and 3D mesh ( right ) [ 34 ]. …”

Section: Figurementioning

confidence: 99%

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Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Vinodkumar,

Karabulut,

Avots

et al. 2024

Entropy

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“…Finally we present a review of previous research work in the field of deep-learning-based single-view 3D reconstruction. taken from [32]. (b) is taken from [33].…”

Section: Literature Reviewmentioning

confidence: 99%

“…From (a) to (f), are voxel grids, octree, point clouds, mesh, implicit functions, and primitive shapes representation respectively. (a)(c)(d) are taken from[32]. (b) is taken from[33].…”

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confidence: 99%

3D reconstruction from single images

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revised the manuscript. me many valuable study strategies. They have all provided as outstanding examples for me to emulate in my academic pursuits.In addition, I would want to take this opportunity to thank NTU for awarding me a scholarship, which has enabled me to concentrate entirely on my study without being distracted by concerns over my ability to pay my bills. In the future, I would like to use what I have learned to make a positive impact on both NTU and the wider community.Throughout the entirety of my studies, I would also want to extend my gratitude to the lab managers, Mr. Alex and Mr. Neo, who assisted me with my equipment and provided me with a great deal of food and delight.Last but not least, I would want to express my gratitude to my parents, who have always been there for me emotionally. Their tenacity and dedication have always been an example to me and a source of motivation for me. My sincere gratitude goes out to my boyfriend. When I was feeling down, he would constantly try to cheer me up and get me out of my stressful situations. My studies for the Ph.D. are made more enjoyable by him.

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The use of CNNs in VR/AR/MR/XR: a systematic literature review

Cortes,

Bermejo,

Juiz

2024

Virtual Reality

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This study offers a systematic literature review on the application of Convolutional Neural Networks in Virtual Reality, Augmented Reality, Mixed Reality, and Extended Reality technologies. We categorise these applications into three primary classifications: interaction, where the networks amplify user engagements with virtual and augmented settings; creation, showcasing the networks’ ability to assist in producing high-quality visual representations; and execution, emphasising the optimisation and adaptability of apps across diverse devices and situations. This research serves as a comprehensive guide for academics, researchers, and professionals in immersive technologies, offering profound insights into the cross-disciplinary realm of network applications in these realities. Additionally, we underscore the notable contributions concerning these realities and their intersection with neural networks.

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A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh

Cited by 7 publications

References 19 publications

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

3D reconstruction from single images

The use of CNNs in VR/AR/MR/XR: a systematic literature review

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