- PAMI2024
- mmWave
- conference
- mmWave
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Photometric Stereo
Photometric stereo is a revolutionary technique for 3D surface modeling. It captures images from multiple viewpoints and aligns corresponding points to construct a three-dimensional representation of an object's surface. This technique has high accuracy and can handle complex and irregular surfaces with ease. It is also versatile, fast, and cost-effective. Photometric stereo can be applied to a wide range of scenarios, from indoor environments to outdoor landscapes. Its real-world applications include autonomous driving, robotics, and augmented reality.
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Patch-based MVS
Patch-based MVS (Multi-View Stereo) is a new approach for 3D surface modeling that leverages the power of images, infrared (IR) technology, and laser scanning to create high-fidelity 3D models. This innovative method automates the process of creating 3D models from scratch, making it faster, more accurate, and cost-effective than traditional methods. Patch-based MVS can handle complex shapes and structures, generate multiple views of an object from different angles, and be used in various industries such as automotive design, fashion design, aerospace engineering, medical imaging, and virtual reality. However, challenges like computational complexity, data privacy concerns, and ethical considerations need to be addressed to achieve widespread adoption. Future directions include hardware acceleration, data privacy enhancements, and humanization of AI models.
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Object Detection and Avoidance
In 3D surface modeling, object detection and avoidance are crucial for efficient and accurate modeling. Feature extraction, machine learning algorithms, and deep learning models are used for object detection, while path planning, motion planning, and collision detection algorithms ensure safe and efficient object avoidance. These technologies continue to evolve, providing advanced solutions for object detection and avoidance in 3D surface modeling.
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NeRF (Neural Radiance Fields)
NeRF is a revolutionary approach to 3D surface modeling using deep learning techniques. It models surfaces in a more efficient and accurate way by capturing complex interactions between light and surfaces. NeRF can generate highly realistic 3D representations of objects, environments, and scenes without requiring physical models or pre-defined geometry. The architecture consists of the radiance field network and the volume rendering network, both built with convolutional layers and recurrent layers. NeRF has numerous applications in computer graphics, robotics, and other fields that involve understanding and manipulating 3D objects, such as scene generation, object tracking, and visual navigation.
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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.