- PAMI2024
- mmWave
- conference
- mmWave
•
•
•
-
Horn's Contributions to SFS
Horn's contributions to surface modeling have been significant in the field of computer vision. His work on three-dimensional (3D) surface modeling, which involves creating accurate representations of surfaces using image, infrared, and laser technologies, has revolutionized the way we analyze and understand complex objects. In this article, we will review some of Horn's key contributions to surface modeling and explore how they have influenced the development of modern techniques. Image-based Surface Modeling, One of Horn's early contributions to surface modeling was the development of image-based techniques for creating 3D models from digital images. These methods involve using computer vision algorithms to extract features from the images, such as edges, corners, and textures, and then reconstructing the 3D surface from these features. Some popular image-based surface modeling techniques include Feature Extraction, Surface Reconstruction, and Mesh Processing. Infrared Surface Modeling, Horn also made significant contributions to surface modeling using infrared sensors. Infrared surface mapping (ISR) is a technique that uses infrared cameras to capture images of surfaces and create 3D models of those surfaces. ISR has several advantages over other surface modeling methods, such as being able to operate in challenging environments with limited visibility or lighting conditions. Some key aspects of ISR include Image Acquisition, Feature Extraction, and Surface Reconstruction. Laser Scanning Surface Modeling, Another area where Horn's contributions were significant was in laser scanning (LS) surface modeling. LS involves using laser scanners to capture detailed measurements of surfaces in three dimensions. These measurements can be used to create highly accurate 3D models of the surfaces. Some key aspects of LS include Scanner Design, Point Cloud Processing, and Surface Reconstruction.
-
Horn's SFS Framework
3D surface modeling is a crucial step in many applications of computer vision and robotics. Horn's SFS (Surface Feature Extraction) framework is a powerful tool for 3D surface modeling based on image processing techniques. It consists of four steps，image preprocessing, feature extraction, feature clustering, and surface reconstruction. Horn's SFS framework has several advantages over other feature extraction methods and has numerous applications in computer vision and robotics, including object recognition, tracking, manipulation, and medical imaging.
-
Historical Development and Main Contributors
3D surface modeling involves creating accurate three-dimensional representations of physical surfaces using digital tools. Image, infrared, and laser technologies have revolutionized the field. Michael Bommarito's photogrammetry was a key early development. Digital terrain model (DTM) was developed by David F. Rogers. Image-based 3D surface modeling has been widely used in industrial applications. IR 3D surface modeling is advantageous over traditional imaging methods. Laser-based 3D surface modeling is flexible and accurate, but challenges remain with non-uniform surfaces.
-
High-Speed 3D Scanning
High-speed 3D scanning is a game-changing technology in engineering, product development and design. It uses advanced sensors and software to capture an object's surface in real-time, creating highly accurate and detailed 3D models of surfaces. Laser scanning, optical scanning, and infrared scanning are some of the high-speed 3D scanning technologies available. Laser scanning is fast but expensive, optical scanning is versatile but slower, and infrared scanning is new but offers high resolution and speed. These technologies have applications in various industries such as manufacturing, architecture, healthcare, and automotive.
-
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.