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 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 keypoints and depth maps to determine depth priors.
- Performing initialization and depth-regularized optimization.
Potential Applications: This technology could be used in augmented reality, virtual reality, robotics, autonomous vehicles, and 3D modeling applications.
Problems Solved: This technology addresses the challenges of accurately reconstructing 3D scenes from 2D images, especially in dynamic or complex environments.
Benefits: The benefits of this technology include improved accuracy in 3D reconstruction, better depth estimation, and enhanced performance in various computer vision tasks.
Commercial Applications: Potential commercial applications include developing advanced camera systems, creating immersive virtual experiences, enhancing navigation systems, and improving object recognition in AI.
Prior Art: Prior research in computer vision, image processing, and 3D reconstruction can provide valuable insights into similar technologies and approaches.
Frequently Updated Research: Stay updated on advancements in computer vision algorithms, depth estimation techniques, and optimization methods for structure from motion systems.
Questions about Depth-Guided Structure from Motion: 1. How does this technology improve upon traditional structure from motion techniques? 2. What are the limitations of using depth priors in optimizing 3D reconstruction processes?
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.
