RIA: Detailed Three-Dimensional Model-Building from Image Sequences

RIA：根据图像序列构建详细的三维模型

• 批准号: 9308415
• 负责人: William Seales
• 金额: $ 10万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-12-15 至 1998-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9308415&HistoricalAwards=false
• 关键词: RIA; Detailed; Three; Dimensional; Model

9308415 Seales This is the first-year funding of a three-year continuing Research Initiation Award. The long-term goal of this research is to develop and implement new strategies for building complete models of three-dimensional objects from video sequences. Efficient 3D model-building capabilities are fundamental for mobile robot vision tasks such as learning new shapes, manipulating intricate objects, navigating around obstacles, and recognizing a known shape. A central focus of this project is the analysis of the problem of recovering global three-dimensional shape information from an image sequence under minimal viewing conditions using image curves that are formed from both smooth and non-smooth components of three- dimensional shapes. The viewing situations include two main scenarios: active feedback strategies where information in the image sequence is used to control the camera system; and uncalibrated camera rigs where camera parameters are unknown. This work is based upon an analysis of the behavior of projected curves in image sequences and their relationship to three-dimensional shape. This research will provide a deeper theoretical and empirical understanding that will lead to the development of efficient, implemented and tested strategies for building detailed models of three-dimensional objects under minimal viewing conditions.

9308415 Seales 这是为期三年的持续研究启动奖的第一年资助。 这项研究的长期目标是开发和实施新策略，从视频序列中构建三维对象的完整模型。 高效的 3D 模型构建功能是移动机器人视觉任务的基础，例如学习新形状、操纵复杂物体、绕过障碍物以及识别已知形状。 该项目的中心焦点是分析在最小观看条件下使用由三维形状的平滑和非平滑分量形成的图像曲线从图像序列恢复全局三维形状信息的问题。观看情况包括两个主要场景：主动反馈策略，其中图像序列中的信息用于控制相机系统；以及相机参数未知的未经校准的相机装备。这项工作基于对图像序列中投影曲线的行为及其与三维形状的关系的分析。 这项研究将提供更深入的理论和实证理解，从而开发出有效的、可实施的和经过测试的策略，用于在最小观看条件下构建三维物体的详细模型。