Amazon technologies, inc. (20240346686). DEPTH-GUIDED STRUCTURE-FROM-MOTION TECHNIQUES simplified abstract
Contents
DEPTH-GUIDED STRUCTURE-FROM-MOTION TECHNIQUES
Organization Name
Inventor(s)
Xiaohan Nie of Lynnwood WA (US)
Michael Thomas Pecchia of Los Angeles CA (US)
Leo Chan of Bowen Islands (CA)
Ahmed Aly Saad Ahmed of Bothell WA (US)
Muhammad Raffay Hamid of Seattle WA (US)
DEPTH-GUIDED STRUCTURE-FROM-MOTION TECHNIQUES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346686 titled 'DEPTH-GUIDED STRUCTURE-FROM-MOTION TECHNIQUES
Simplified Explanation: The patent application describes a system for depth-guided structure from motion, which involves obtaining image frames, determining keypoints, estimating dense depth maps, and using depth priors for optimization.
- **Key Features and Innovation:**
- Obtaining image frames from a digital content item. - Determining 2D keypoints for the image frames. - Estimating dense depth maps using a depth estimator. - Using depth priors for initialization and optimization.
- **Potential Applications:**
- Augmented reality applications. - 3D modeling and reconstruction. - Robotics and autonomous navigation systems.
- **Problems Solved:**
- Improving accuracy and efficiency of structure from motion. - Enhancing depth estimation in complex scenes. - Facilitating 3D reconstruction from 2D images.
- **Benefits:**
- Enhanced depth perception in computer vision systems. - Improved scene understanding and object recognition. - Streamlined optimization processes for 3D reconstruction.
- **Commercial Applications:**
- Title: "Advanced Depth-Guided Structure from Motion Technology for Enhanced Visual Applications" - This technology can be utilized in industries such as gaming, virtual reality, and surveillance for more accurate and detailed visual representations.
- **Prior Art:**
- Researchers in the field of computer vision and robotics have explored similar techniques for depth estimation and structure from motion. Relevant papers include those on keypoint detection and depth map generation.
- **Frequently Updated Research:**
- Ongoing research in computer vision and machine learning is continuously improving depth estimation algorithms and optimizing structure from motion techniques for various applications.
Questions about Depth-Guided Structure from Motion: 1. What are the key challenges in implementing depth-guided structure from motion technology in real-time applications? 2. How does the use of depth priors enhance the accuracy of 3D reconstruction in complex scenes?
Original Abstract Submitted
systems, devices, and methods are provided for depth-guided structure from motion. a system may obtain a plurality of image frames from a digital content item that corresponds to a scene and determine, based at least in part on a correspondence search, a set of 2-d keypoints for the plurality of image frames. a depth estimator may be used to determine a plurality of dense depth map for the plurality of image frames. the set of 2-d keypoints and the plurality of dense depth maps may be used to determine a corresponding set of depth priors. initialization and/or depth-regularized optimization may be performed using the keypoints and depth priors.
