- PAMI2024
- mmWave
- conference
- mmWave
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Multi-View Stereo (MVS)
Multi-View Stereo (MVS) is a powerful technique for 3D surface modeling that captures images from multiple viewpoints and combines them to create a 3D model. The process involves image capture, registration, and surface reconstruction. MVS offers advantages such as multiple viewpoints, robustness, scalability, and cost-effectiveness. However, it has limitations like limited accuracy, computational cost, and object shape change. MVS requires careful evaluation of its suitability for specific applications.
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Medical Imaging
Since its inception, medical imaging has come a long way with the advancement of technology. 3D surface modeling is one such advancement that allows for the creation of detailed models of internal organs and tissues using images, infrared, and lasers. Image-based 3D models are non-invasive, quick, and cost-effective, but accuracy may be limited by image quality. Infrared 3D surface modeling uses infrared cameras to capture thermal radiation emitted by objects, providing insights into disease or injury. Laser-based 3D surface modeling creates detailed cross-sectional images of structures like blood vessels and nerves, providing high-resolution images with minimal invasiveness. Together, these techniques offer a powerful tool for diagnosis and treatment planning in medicine.
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Marr-Poggio Stereo Matching
The Marr-Poggio stereo matching method is a widely used technique for depth mapping in computer vision applications. It involves creating a depth map from a single camera image by estimating the correspondences between pixels in the two images based on the epipolar geometry model. The method has several advantages, including computational efficiency and robustness to image noise and lighting variations. However, it also has limitations, such as assuming no motion between cameras and not handling perspective distortion well. The Marr-Poggio method has numerous applications in computer vision, including object recognition and tracking.
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Light Intensity Estimation
This article explores various techniques for estimating light intensity using image, infrared, and laser technologies. Image-based methods rely on analyzing pixel values to estimate relative light intensity changes. Infrared sensors capture images of objects in dark or low-light conditions, with algorithms like Dark Object Removal identifying illuminated regions for high-resolution 3D reconstruction. Laser technology offers precise measurements of light intensity and color temperature, with Time-of-Flight methods calculating distance and reflectivity/transmissivity for 3D surface modeling. By combining these techniques, accurate 3D models can be created capturing the true essence of our physical world.
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Kinect and Structured Light Technology
In recent years, 3D surface modeling has seen a significant transformation thanks to the advancements in image recognition, infrared sensing, laser scanning, and Microsoft Kinect and Structured Light (SL) technology. The Kinect's motion tracking capabilities and SL's high-resolution imaging make it possible to create highly accurate and detailed 3D models of objects or scenes in real-time. This integration is particularly useful for applications that require both human interaction and static imagery. These technologies have numerous potential applications in various industries such as product design, virtual reality, art and architecture, medical imaging, etc.