- PAMI2024
- mmWave
- conference
- mmWave
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Structured Light and TOF Sensors
Structured Light and Time-of-Flight (TOF) sensors are two advanced imaging technologies used in 3D surface modeling. Structured light sensors capture images by projecting a pattern of structured light onto an object, creating high-resolution 3D models from different viewpoints. Zebrascan is one example of a structured light sensor used in industrial inspection and quality control applications. Time-of-Flight sensors measure the time it takes for a beam of light to travel between two points on an object's surface, creating accurate 3D maps of complex surfaces. TOF sensors have numerous applications in industrial inspection, quality control, robotics, and autonomous vehicles.
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Stereo Vision
Stereo vision is a cutting-edge technology that has revolutionized 3D surface modeling by providing accurate and detailed information about the physical properties of objects. By combining various imaging techniques such as image, infrared, and laser, stereo vision systems can create high-resolution 3D models of real-world surfaces. Stereo vision has numerous applications in various industries, including manufacturing, automotive, healthcare, and consumer electronics. Stereo vision systems can be implemented using various imaging techniques based on image, infrared, and laser technologies. The future prospects of stereo vision include integration with other technologies and increased automation.
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Stereo Matching
Stereo matching is a powerful technique that enables engineers to create high-fidelity 3D models from single images or videos. This article explores the concept of stereo matching, its applications, and the key technologies involved in this process. Stereo matching is a technique used to reconstruct the geometry of an object from two or more images taken from different angles. There are several approaches to stereo matching, but the most common ones involve using either a disparity map or a depth map. One of the key advantages of stereo matching is its ability to handle complex scenes with multiple objects and varying lighting conditions.
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Shape from Texture (SFT)
Shape from Texture (SFT) is a deep learning approach that uses convolutional neural networks to learn 3D surface representations from single 2D texture images. SFT relies on a feature extractor and decoder, with challenges in designing an efficient feature extractor and choosing loss functions and optimization algorithms. Applications of SFT include object detection, shape retrieval, image generation, and medical imaging. While challenges exist, SFT has shown promising results and holds potential for future developments in 3D surface modeling.
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Shape from Shading (SFS)
This paper introduces Shape from Shading (SFS), a novel approach to 3D surface modeling that uses deep learning algorithms to generate high-quality surfaces from images or videos. SFS learns a mapping function by training a convolutional neural network on a dataset of 2D textures and corresponding 3D surfaces. Once the mapping function is learned, it can be used to generate new surfaces by sampling random points in the 3D space and applying the learned mapping function to the corresponding 2D texture. The applications of SFS are diverse, including architecture, engineering, and entertainment.