Recovery of 3D Information from 3D Range Data and from 2D/3D Optical Flow and 3D Scene/Range Flow

从 3D 距离数据以及 2D/3D 光流和 3D 场景/距离流恢复 3D 信息

• 批准号: RGPIN-2017-06497
• 负责人: Barron, John
• 金额: $ 0.77万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=674720
• 关键词: Recovery; 3D; Information; Range; Data

The computation of motion via 2D/3D optical flow and 3D scene/range flow is my long term primary research interest. I have an additional primary interest in in applying these and other computational techniques to measure 3D plant growth from 3D range data in a non-invasive, non-contact manner. Over the next 5 years, my key short term primary research objectives are:***(1) To measure accurate 2D optical flow at occlusion boundaries in real image data. Currently regularization approaches produce flow that tends to "bleed" across occlusion edges (where the images of two differently moving objects meet). We propose to compute the flow inside of closed segmented regions to produce flow that (correctly) abruptly changes at occlusion boundaries.***(2) To measure accurate 3D scene/range flow (3D optical flow on surfaces) on stereo and range image sequences. This type of 3D optical flow measures the 3D motion of visible parts of a scene using depth maps produced from stereo images (scene flow) or directly measured using a range sensor. We hope to produce real time scene/range flow that would be useful in car driving, for example.***(3) To measure 3D quantitative plant growth from 3D range data of growing plants in a non-invasive, non-contact manner. This will allow the quantitative evaluation of a plant's growth over its life cycle. One application would be to quantify the growth rate of wild type and genetically modified plants.

通过二维/三维光流和三维场景/距离流计算运动是我长期的主要研究兴趣。我有一个额外的主要兴趣在应用这些和其他计算技术来测量三维植物生长从三维范围数据在一个非侵入性的，非接触的方式。在接下来的5年里，我的主要短期研究目标是：*（1）在真实的图像数据中精确测量遮挡边界处的二维光流。目前的正则化方法产生的流往往会在遮挡边缘（两个不同移动对象的图像相遇的地方）“流血”。我们建议计算封闭分割区域内部的流量，以产生在遮挡边界处（正确地）突然变化的流量。(2)在立体和距离图像序列上测量精确的3D场景/距离流（表面上的3D光流）。这种类型的3D光流使用从立体图像（场景流）产生的深度图或使用距离传感器直接测量的深度图来测量场景的可见部分的3D运动。我们希望产生真实的时间场景/范围流，例如在汽车驾驶中有用。(3)以非侵入、非接触的方式从生长植物的三维范围数据测量三维定量植物生长。这将允许定量评估植物在其生命周期中的生长。一个应用是量化野生型和转基因植物的生长速率。