Quality-driven autonomous 3D reconstruction of large-scale scenes

在过去的30年里，平板电脑和电视一直是占主导地位的显示设备。它们只能显示2D内容，这可以很容易地使用今天的数码相机获得。随着虚拟现实（VR）设备的实用化和普及，允许用户从选定的视点和方向观看3D内容，因此，一个重要的问题是如何有效和经济地捕获3D对象和场景。用户可以使用范围扫描仪从不同侧面扫描给定的对象，然后将3D点云配准在一起以生成表面模型。在这方面，我和我的合作者开发了一种最先进的算法，通过使用机械臂将扫描仪定位在战略选择的位置来自动化扫描过程。然而，如何以自主方式获取大规模户外场景的高质量模型仍然是一个悬而未决的问题。大型场地的3D重建在城市规划，地质和考古调查，虚拟旅游和军事模拟。它是许多研究领域的一个活跃话题，包括计算机视觉、图形学、机器人、土木工程和遥感。在过去，人们经常求助于载人机载LiDAR系统，这带来了令人生畏的成本。由于无人机（UAV）的快速发展，现在可以以更经济的方式扫描大型场地，本研究旨在开发新的、高效的、自主的大规模三维场景重建技术。为了实现这一目标，在接下来的五年里，我和我的学生将研究：1）如何使用配备LiDAR的无人机对大型户外场景进行质量引导的自主3D重建; 2）如何使用仅具有图像捕获能力的低成本现成无人机实现相同的目标; 3）让两种类型的无人机协同工作并将图像和范围数据融合在一起的好处;（4）从点云数据中分割出运动物体并重建运动物体的动态4D时空模型的可行性。随着虚拟现实设备的广泛应用，对大规模场景模型的需求将急剧增加。因此，本研究中开发的技术将在上述领域产生广泛的影响，并使不同的研究群体受益。他们很可能通过使我们的城市和环境的高质量3D模型容易获得来推进信息系统和技术领域。一些研究问题需要解决，这将有助于培训HQP采取在加拿大的高科技产业的高度需求的职位。

Monitors and TVs have been the dominating display devices in the past 30 years. They are only capable of showing 2D contents, which can be easily acquired using today's digital cameras. As virtual reality (VR) devices gain practicality and popularity, allowing users to view 3D contents from selected viewpoints and directions, an important question is, therefore, how to efficiently and economically capture objects and scenes in 3D.The technology for acquiring 3D shapes of small objects is already mature. Users can scan a given object from different sides using a range scanner and then register together the 3D point clouds to generate a surface model. On this front, my collaborators and I have developed a state-of-the-art algorithm that automates the scanning process through positioning a scanner at strategically selected locations using a robotic arm. However, how to acquire high-quality models for large-scale outdoor scenes in an autonomous manner is still an open problem.3D reconstruction for large sites has vast applications in areas such as urban planning, geological and archaeological survey, virtual tourism, and military simulation. It is an active topic in many research communities, including computer vision, graphics, robotics, civil engineering, and remote sensing. In the past, people often resorted to manned airborne LiDAR systems, which come with intimidating cost. Thanks to the rapid development of unmanned aerial vehicles (UAVs), it is now possible to scan large sites in a much more economical manner.The proposed research aims at developing novel, efficient, and autonomous techniques for large-scale 3D scene reconstruction. To achieve this goal, in the next five years my students and I will investigate: 1) how to perform quality-guided autonomous 3D reconstruction for large outdoor scenes with LiDAR-equipped UAVs; 2) how to achieve the same objective using low-cost off-the-shelf UAVs with only image capture capability; 3) the benefits of letting both types of UAVs work collaboratively and fusing together image and range data; and 4) the feasibility of segmenting moving objects from point clouds and reconstructing dynamic 4D spatial-temporal models for these moving objects.As VR devices get widely adopted, the demands for large-scale scene models are expected to increase dramatically. Hence, the techniques to be developed in this research will make broad impacts in aforementioned areas and benefit different research communities. They are likely to advance the Information Systems and Technology field through making high-quality 3D models of our cities and environments easily obtainable. A number of research questions need to be addressed, which will help to train HQPs to take up highly demanded positions in Canada's high tech industries.