- PAMI2024
- mmWave
- conference
- mmWave
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Deformable Models for SFT
Deformable models for surface-based targeting (SFT) use mathematical representations of physical objects that can change their shape over time. These models are designed to adapt to the shape and movement of the target, making them more effective at tracking and targeting. Mesh generation, parametric modeling, and optical flow analysis are some of the techniques used to generate deformable models. The use of deformable models for SFT has several advantages, including improved target tracking, better target recognition, and increased target localization.
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Deep Learning and 3D Modeling
The integration of deep learning techniques has revolutionized the field of 3D modeling. Deep learning, a subfield of machine learning, enables the creation of complex models that can analyze large amounts of data and make accurate predictions. Image-based surface modeling with deep learning involves training a neural network to recognize patterns in images and create a 3D model from them. Infrared and laser-based surface modeling with deep learning is used to create detailed 3D models based on measurements taken using infrared or laser sensors. Deep learning has emerged as a powerful tool for 3D modeling, particularly when it comes to surface modeling using images, infrared, and laser data.
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Cultural Heritage Preservation
Cultural heritage preservation through 3D surface modeling using image, infrared, and laser technology is a crucial aspect of maintaining the identity and history of a society. Image-based 3D models capture images of cultural heritage sites, while infrared cameras reveal details invisible to the naked eye. Laser scanners create precise measurements of complex structures, providing accurate digital models for research and conservation purposes. 3D surface modeling ensures future generations have access to these important cultural artifacts and historical sites. As technology advances, we can expect even more sophisticated methods for creating 3D models that will revolutionize cultural heritage preservation.
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Challenges and Future Directions
In recent years, 3D surface modeling with image, infrared, and laser techniques has seen tremendous advancements. However, challenges remain in data collection, preprocessing, and model generation. Future research directions include deep learning-based models, multimodal data integration, and unsupervised learning approaches. These developments hold promise for improving the accuracy and efficiency of 3D surface modeling, enabling new applications across various industries.
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Autonomous Driving
Autonomous driving is rapidly advancing, and 3D surface modeling is a game-changer. By capturing detailed digital representations of objects and environments, self-driving cars can detect obstacles, plan routes, avoid collisions, and navigate indoor spaces with greater accuracy. Photogrammetry, laser scanning, and stereoscopic photography are key surface modeling techniques. As technology improves, autonomous driving will become safer, more efficient, and more accessible to the public.