Venice2020 Building Heights Detection: Difference between revisions
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= Motivation = | = Motivation = | ||
Building height is one of the most important information to consider for urban planning, economic analysis, digital twin implementation, and so on. For Time Machine implementation, it is important to compare different historical data and current data based on different times. Drones and Google Maps provide 3D views of buildings and surroundings in detail which are useful resources to keep track of changes in a target place. In this project, we aim to detect the building heights of Venice. Many geographical areas, which are either not famous or not accessible easily are accessible by Google Earth 3D views. We aim to make a 3D model of Venice with every detail of the city and calculate point clouds to detect the heights of the city. This information can be used to understand current details of the city and also will be useful in the future as historical data to compare with. | |||
= Milestones = | = Milestones = | ||
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=== Milestone 2 === | === Milestone 2 === | ||
* Align photos of the same location, derive sparse point clouds made up of only high-quality tie points and repeated optimize the camara model by reconstruction uncertainty filtering, projection accuracy filtering and reprojection error filtering. | * Align photos of the same location in Venice, derive sparse point clouds made up of only high-quality tie points and repeated optimize the camara model by reconstruction uncertainty filtering, projection accuracy filtering and reprojection error filtering. | ||
* Build dense point clouds using the estimated camera positions generated during sparse point cloud based matching and the depth map for each camera | * Build dense point clouds using the estimated camera positions generated during sparse point cloud based matching and the depth map for each camera | ||
* Evaluate point clouds objective quality and select high quality models by point-based approaches which can assess both geometry and color distortions | |||
=== Milestone 3 === | === Milestone 3 === | ||
* | |||
* Align and compose dense point clouds of different spots to generate an integrated Venice dense cloud model | |||
* Build Venice 3D model and tilted model according to dense point cloud data | |||
=== Milestone 4 === | === Milestone 4 === | ||
Revision as of 14:41, 24 November 2021
Introduction
The main goal of our project is to obtain the height information of buildings in Venice. In order to achieve this goal, we construct a point cloud model of Venice with drone video from youtube and google earth with the help of a photogrammetry tool developed at DHLAB.
Motivation
Building height is one of the most important information to consider for urban planning, economic analysis, digital twin implementation, and so on. For Time Machine implementation, it is important to compare different historical data and current data based on different times. Drones and Google Maps provide 3D views of buildings and surroundings in detail which are useful resources to keep track of changes in a target place. In this project, we aim to detect the building heights of Venice. Many geographical areas, which are either not famous or not accessible easily are accessible by Google Earth 3D views. We aim to make a 3D model of Venice with every detail of the city and calculate point clouds to detect the heights of the city. This information can be used to understand current details of the city and also will be useful in the future as historical data to compare with.
Milestones
Milestone 1
- Get familiar with OpenMVG, Open3D, Agisoft Metashape, Blender, CloudCompare and QGIS
- Collect high resolution venice drone videos on youtube and record Google Earth birdview videos as supplementary materials
Milestone 2
- Align photos of the same location in Venice, derive sparse point clouds made up of only high-quality tie points and repeated optimize the camara model by reconstruction uncertainty filtering, projection accuracy filtering and reprojection error filtering.
- Build dense point clouds using the estimated camera positions generated during sparse point cloud based matching and the depth map for each camera
- Evaluate point clouds objective quality and select high quality models by point-based approaches which can assess both geometry and color distortions
Milestone 3
- Align and compose dense point clouds of different spots to generate an integrated Venice dense cloud model
- Build Venice 3D model and tilted model according to dense point cloud data
Milestone 4
Planning
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