- PAMI2024
- mmWave
- conference
- mmWave
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Introduction of Neural Networks
Neural networks, inspired by the human brain, are a type of artificial intelligence. They consist of interconnected nodes or neurons, organized into layers. There are 3 types, feedforward, recurrent, and convolutional networks. Neural networks can be used for 3D surface modeling in areas such as surface reconstruction, segmentation, and completion. By combining techniques like autoencoders, CNNs, and GANs, state-of-the-art results can be achieved.
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Interactive 3D Environments
Interactive 3D environments are becoming increasingly common in the field of computer graphics, providing users with immersive experiences. Surface modeling is a key technology for creating these environments, using techniques such as image, infrared (IR), and laser scanning. Image-based surface modeling captures images of physical objects to create digital models, while IR-based modeling can capture details like reflections and refractions. Laser-based surface modeling creates detailed measurements of surfaces, with advantages including high accuracy and resolution. These techniques have applications in areas such as medical imaging, automotive testing, and industrial design. As research continues to advance, we can expect more realistic and sophisticated interactive 3D environments.
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Infrared Light Pattern Projection
This article discusses the use of infrared (IR) light pattern projection in 3D surface modeling. IR light pattern projection works by illuminating an object with IR light and observing the scattered light patterns emitted by the material's surface, which can provide valuable information about the surface topography, texture, and composition of the material. There are two main types of IR light pattern projections, diffuse reflection and specular reflection. IR light pattern projections have numerous applications in 3D surface modeling, including surface topography analysis, material classification, and in-depth inspection. Compared to traditional methods such as laser scanning and photogrammetry, IR light pattern projections offer lower costs, higher accuracy, and flexibility.
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Increasing Computational Efficiency
This article discusses the use of image, infrared, and laser technologies to improve computational efficiency in 3D surface modeling. Image-based surface modeling uses images as input data and can handle large amounts of data quickly and efficiently. Infrared surface modeling provides high-resolution information about surfaces without damaging or altering them, while laser surface modeling can provide high-speed and high-accuracy measurements over large areas. Each method has its own strengths and weaknesses depending on the specific application.
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Implicit Scene Representation
Implicit Scene Representation is a novel approach to 3D surface modeling that employs deep learning techniques to generate high-quality 3D models from raw data such as images, infrared (IR) scans, and laser scans. This method learns a mapping function that maps the input data into a low-dimensional latent space, where it captures the underlying structure and patterns of the data. By combining different types of data, this approach can produce more robust and accurate 3D models. However, it still faces challenges such as lack of annotated data and specialized hardware and software tools. Despite these limitations, Implicit Scene Representation holds great potential in various applications such as robotics, augmented reality, and virtual reality.