- PAMI2024
- mmWave
- conference
- mmWave
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High-Quality 3D Rendering
High-Quality 3D rendering has come a long way since its inception. Image, IR and Laser-based Surface Modeling have revolutionized the field. Image-based rendering is ideal for VR/AR applications, while IR-based rendering works with difficult to model materials. Laser-based rendering is ideal for reflective surfaces like glass and metal. However, each technique has limitations, such as accuracy, speed and cost. Future directions include improved camera sensors and machine learning algorithms.
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Handling Occlusions and Shadows
Handling occlusions and shadows is a crucial challenge in 3D surface modeling. Image-based techniques, infrared (IR) techniques, and laser scanning techniques are used to handle occlusions effectively. Shadow casting, edge detection, and volumetric lighting are used to handle shadows. The ability to handle occlusions and shadows is essential in various applications such as virtual reality (VR), augmented reality (AR), and industrial design.
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Furukawa-Ponce Patch-based MVS
The Furukawa-Ponce Patch-based MVS (Multi-View Stereo) method is a powerful approach for 3D surface modeling. It relies on multiple views of an object to reconstruct its geometry and texture. Each pixel in the image is represented as a local patch, which captures the characteristics of nearby pixels. This method is robust to variations in viewpoints and lighting conditions, but it can be computationally expensive for large datasets or complex scenes. The accuracy of the reconstruction depends on accurate calibration of cameras and sensors. An example application of this method is 3D surface modeling of a car body using images captured by two cameras.
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Feature Matching Algorithms
This blog post explores feature matching algorithms in 3D surface modeling, focusing on three popular techniques,RANSAC, FLANN, and EPNP. RANSAC works by iteratively selecting random features to fit a plane and comparing them with ground truth. FLANN is an efficient library for searching nearest neighbors in large datasets used to find the nearest features between two sets of points. EPNP uses a multi-view approach to estimate the pose of one surface relative to another by minimizing Euclidean distance between their corresponding points in both views. Understanding these algorithms' principles can help create high-quality 3D models from raw data.
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Evolution of 3D Surface Modeling
The evolution of 3D surface modeling techniques has been remarkable, from the early days of computer graphics to modern applications in various domains such as architecture, product design, and medical imaging. Key milestones, advancements, and future prospects include the development of photogrammetry for large-scale scanning, breakthroughs in deep learning methods for complex surface features, and the integration of 3D printing technology with virtual reality and augmented reality. The continuous improvement of 3D surface modeling will undoubtedly lead to more innovative and practical applications in diverse fields.