Laptop Generated Quality Report Phase 1 Time 00h:26m:45s Phase 2 Time 02h:30m:06s Phase 3 Time 01h:20m:19s Total Time All phases 04h:17m:10s

Similar documents
28 out of 28 images calibrated (100%), all images enabled. 0.02% relative difference between initial and optimized internal camera parameters

Paris-Le Bourget Airport. 557 out of 557 images calibrated (100%), all images enabled

Quality Report Generated with Pix4Dmapper Pro version

Near-Infrared Dataset. 101 out of 101 images calibrated (100%), all images enabled

Processed :36:54 Average Ground Sampling Distance (GSD) Time for Initial Processing (without report)

Quality Report Generated with Pro version

Quality Report Generated with Pix4Dmapper Pro version

Quality Report Generated with Postflight Terra 3D version

Quality Report Generated with version

Quality Report Generated with version

Quality Report Generated with Pix4Ddiscovery version

Quality Report Generated with Pro version

Getting Started with Pix4D for Agriculture 3.3

Drone2Map: an Introduction. October 2017

Tutorial (Beginner level): Orthomosaic and DEM Generation with Agisoft PhotoScan Pro 1.3 (with Ground Control Points)

Tutorial (Beginner level): Orthomosaic and DEM Generation with Agisoft PhotoScan Pro 1.3 (without Ground Control Points)

2. POINT CLOUD DATA PROCESSING

Photogrammetric Performance of an Ultra Light Weight Swinglet UAV

Drone2Map for ArcGIS: Bring Drone Imagery into ArcGIS. Will

Introduction. Acute3D S.A.S. WTC Valbonne Sophia Antipolis. 120 route des Macarons.

Simply powerful. Pix4Dmapper features the raycloud. Read more on Next generation aerial image processing software

Geometry of Aerial photogrammetry. Panu Srestasathiern, PhD. Researcher Geo-Informatics and Space Technology Development Agency (Public Organization)

DENSE 3D POINT CLOUD GENERATION FROM UAV IMAGES FROM IMAGE MATCHING AND GLOBAL OPTIMAZATION

TRAINING MATERIAL HOW TO OPTIMIZE ACCURACY WITH CORRELATOR3D

Mosaicking Software: A comparison of various software suites. Geosystems Research Institute Report 5071

A New Protocol of CSI For The Royal Canadian Mounted Police

SimActive and PhaseOne Workflow case study. By François Riendeau and Dr. Yuri Raizman Revision 1.0

GEOSYSTEMS... 2 UAV Workflow ERDAS IMAGINE UAV Feature Overview Section ERDAS IMAGINE UAV Feature... 2

Accuracy Assessment of an ebee UAS Survey

Camera Drones Lecture 3 3D data generation

Multiray Photogrammetry and Dense Image. Photogrammetric Week Matching. Dense Image Matching - Application of SGM

Producing Ortho Imagery In ArcGIS. Hong Xu, Mingzhen Chen, Ringu Nalankal

Generating highly accurate 3D data using a sensefly exom drone

Sasanka Madawalagama Geoinformatics Center Asian Institute of Technology Thailand

VOLUME COMPUTATION OF A STOCKPILE A STUDY CASE COMPARING GPS AND UAV MEASUREMENTS IN AN OPEN PIT QUARRY

TRIMBLE BUSINESS CENTER PHOTOGRAMMETRY MODULE

Accuracy Assessment of POS AVX 210 integrated with the Phase One ixu150

Digital Photogrammetric System. Version 5.3 USER GUIDE. Processing of UAV data

RELEASE NOTES FOR PHOTOMESH 7.5.1

Files Used in this Tutorial

Complex Sensors: Cameras, Visual Sensing. The Robotics Primer (Ch. 9) ECE 497: Introduction to Mobile Robotics -Visual Sensors

Geomatica OrthoEngine Orthorectifying VEXCEL UltraCam Data

Subpixel accurate refinement of disparity maps using stereo correspondences

FOUR-BAND THERMAL MOSAICKING: A NEW METHOD TO PROCESS THERMAL IMAGERY FROM UAV FLIGHT YICHEN YANG YALE SCHOOL OF FORESTRY AND ENVIRONMENTAL STUDIES

PERFORMANCE OF LARGE-FORMAT DIGITAL CAMERAS

Presented at the FIG Congress 2018, May 6-11, 2018 in Istanbul, Turkey

Files Used in this Tutorial

Scalability for Large Photogrammetry Projects

Copyright 2013 by 3Dflow srl. All Rights Reserved.

UAV data acquisition and processing

ADVANCING REALITY MODELING WITH CONTEXTCAPTURE

EVOLUTION OF POINT CLOUD

Automated Air Photo Orthorectification and Mosaicking Geomatica 2015 Tutorial

3D Computer Vision. Depth Cameras. Prof. Didier Stricker. Oliver Wasenmüller

Structure from Motion (SfM) Photogrammetry Data Exploration and Processing Manual

Extracting Elevation from Air Photos

P h a s e O n e i X U - RS A c c u r a c y A n a l y s i s. T h e f o r e f r o n t o f a e r i a l p h o t o g r a p h y

UAV s in Surveying: Integration/processes/deliverables A-Z. 3Dsurvey.si

Chapter 3 Image Registration. Chapter 3 Image Registration

Agisoft PhotoScan Change Log

Dense DSM Generation Using the GPU

Large Scale 3D Reconstruction by Structure from Motion

BE INSPIRED.

Estimating the wavelength composition of scene illumination from image data is an

Fast and robust techniques for 3D/2D registration and photo blending on massive point clouds

CORRELATOR3D TM Whitepaper

Multiview Photogrammetry 3D Virtual Geology for everyone

ENGN2911I: 3D Photography and Geometry Processing Assignment 1: 3D Photography using Planar Shadows

Agisoft PhotoScan User Manual. Professional Edition, Version 1.2

USING UNMANNED AERIAL VEHICLE (DRONE/FLYCAM) TECHNOLOGY IN SURVEY WORK OF PORTCOAST

Automating Data Alignment from Multiple Collects Author: David Janssen Optech Incorporated,Senior Technical Engineer

A Systems View of Large- Scale 3D Reconstruction

E-510. Built-in image stabiliser Excellent dust reduction system 6.4cm / 2.5'' HyperCrystal LCD New image processing engine

Files Used in this Tutorial

Personal Navigation and Indoor Mapping: Performance Characterization of Kinect Sensor-based Trajectory Recovery

UAS to GIS Utilizing a low-cost Unmanned Aerial System (UAS) for Coastal Erosion Monitoring

NX Tutorial - Centroids and Area Moments of Inertia ENAE 324 Aerospace Structures Spring 2015

The raycloud A Vision Beyond the Point Cloud

REFINEMENT OF COLORED MOBILE MAPPING DATA USING INTENSITY IMAGES

AIRPHEN. The Multispectral camera from HIPHEN

arxiv: v1 [cs.cv] 28 Sep 2018

CS395T paper review. Indoor Segmentation and Support Inference from RGBD Images. Chao Jia Sep

Computer Vision 2. SS 18 Dr. Benjamin Guthier Professur für Bildverarbeitung. Computer Vision 2 Dr. Benjamin Guthier

Photogrammetry: DTM Extraction & Editing

Tree height measurements and tree growth estimation in a mire environment using digital surface models

Assessing 3D Point Cloud Fidelity of UAS SfM Software Solutions Over Varying Terrain

NEXTMap World 30 Digital Surface Model

COMPARISON OF LASER SCANNING, PHOTOGRAMMETRY AND SfM-MVS PIPELINE APPLIED IN STRUCTURES AND ARTIFICIAL SURFACES

The use of different data sets in 3-D modelling

Structured Light II. Thanks to Ronen Gvili, Szymon Rusinkiewicz and Maks Ovsjanikov

MASI: Modules for Aerial and Satellite Imagery. Version 3.0 Satellite Modules. Tutorial

Agisoft PhotoScan User Manual. Professional Edition, Version 1.4

NAME :... Signature :... Desk no. :... Question Answer

Advanced Rendering CHAPTER. Render Window. Learning Objectives. Image Pane

POSITIONING A PIXEL IN A COORDINATE SYSTEM

Trimble VISION Positions from Pictures

UAS Campus Survey Project

PERFORMANCE ANALYSIS OF FAST AT FOR CORRIDOR AERIAL MAPPING

Minimizing Noise and Bias in 3D DIC. Correlated Solutions, Inc.

COMPARATIVE CHARACTERISTICS OF DEM OBTAINED FROM SATELLITE IMAGES SPOT-5 AND TK-350

Transcription:

Laptop Generated Quality Report Phase 1 Time 00h:26m:45s Phase 2 Time 02h:30m:06s Phase 3 Time 01h:20m:19s Total Time All phases 04h:17m:10s Generated with Pix4Dmapper Pro - TRIAL version 2.0.104 Important: Click on the different icons for: Help to analyze the results in the Quality Report Additional information about the sections Click here for additional tips to analyze the Quality Report Summary Project Processed Average Ground Sampling Distance (GSD) Construction Site 2016-02-08 21:02:29 3.95 cm / 1.55 in Area Covered 0.0646 km2 / 6.4604 ha / 0.025 sq. mi. / 15.9722 acres 26m:45s Time for Initial Processing (without report) Quality Check Images median of 33665 keypoints per image Dataset 28 out of 28 images calibrated (100%), all images enabled Camera Optimization 0.01% relative difference between initial and optimized internal camera parameters Matching median of 9537.14 matches per calibrated image Georeferencing yes, no 3D GCP Preview Figure 1: Orthomosaic and the corresponding sparse Digital Surface Model (DSM) before densification. Calibration Details Number of Calibrated Images Number of Geolocated Images Initial Image Positions 28 out of 28 28 out of 28

Figure 2: Top view of the initial image position. The green line follows the position of the images in time starting from the large blue dot. Computed Image/GCPs/Manual Tie Points Positions Figure 3: Offset between initial (blue dots) and computed (green dots) image positions as well as the offset between the GCPs initial positions (blue crosses) and their computed positions (green crosses) in the top-view (XY plane), front-view (XZ plane), and side-view (YZ plane). Overlap

Number of overlapping images: 1 2 3 4 5+ Figure 4: Number of overlapping images computed for each pixel of the orthomosaic. Red and yellow areas indicate low overlap for which poor results may be generated. Green areas indicate an overlap of over 5 images for every pixel. Good quality results will be generated as long as the number of keypoint matches is also sufficient for these areas (see Figure 5 for keypoint matches). Bundle Block Adjustment Details Number of 2D Keypoint Observations for Bundle Block Adjustment 283558 Number of 3D Points for Bundle Block Adjustment 109426 Mean Reprojection Error [pixels] 0.168662 Internal Camera Parameters CanonIXUS220HS_4.3_4000x3000 (RGB). Sensor Dimensions: 6.198 [mm] x 4.648 [mm] EXIF ID: CanonIXUS220HS_4.3_4000x3000 Focal Length Principal Point x Principal Point y R1 R2 R3 T1 T2 Initial Values 2839.640 [pixel] 4.400 [mm] 2019.760 [pixel] 3.129 [mm] 1547.000 [pixel] 2.397 [mm] -0.043 0.026-0.006 0.001 0.002 Optimized Values 2839.162 [pixel] 4.399 [mm] 2032.619 [pixel] 3.149 [mm] 1508.701 [pixel] 2.338 [mm] -0.038 0.025-0.007 0.001 0.002 The number of Automatic Tie Points (ATPs) per pixel averaged over all images of the camera model is color coded between black and white. White indicates that, in average, more than 16 ATPs are extracted at this pixel location. Black indicates that, in average, 0 ATP has been extracted at this pixel location. Click on the image to the see the average direction and magnitude of the reprojection error for each pixel. Note that the vectors are scaled for better visualization. 2D Keypoints Table Number of 2D Keypoints per Image Number of Matched 2D Keypoints per Image Median 33665 9537 Min 23121 3760 Max 39352 16038 Mean 32428 10127 3D Points from 2D Keypoint Matches Number of 3D Points Observed In 2 Images 80065 In 3 Images 15667 In 4 Images 5921 In 5 Images 2971 In 6 Images 1676 In 7 Images 1045 In 8 Images 696 In 9 Images 428 In 10 Images 316 In 11 Images 196 In 12 Images 169 In 13 Images 150 In 14 Images 91 In 15 Images 31 In 16 Images 4

2D Keypoint Matches Number of matches 25 222 444 666 888 1111 1333 1555 1777 2000 Figure 5: Top view of the image computed positions with a link between matching images. The darkness of the links indicates the number of matched 2D keypoints between the images. Bright links indicate weak links and require manual tie points or more images. Geolocation Details Absolute Geolocation Variance 0 out of 28 geolocated and calibrated images have been labeled as inaccurate. Min Error [m] Max Error [m] Geolocation Error X [%] Geolocation Error Y [%] Geolocation Error Z [%] - -15.00 0.00 0.00 0.00-15.00-12.00 0.00 0.00 0.00-12.00-9.00 0.00 0.00 0.00-9.00-6.00 0.00 0.00 0.00-6.00-3.00 10.71 3.57 0.00-3.00 0.00 39.29 50.00 50.00 0.00 3.00 35.71 39.29 50.00 3.00 6.00 14.29 7.14 0.00 6.00 9.00 0.00 0.00 0.00 9.00 12.00 0.00 0.00 0.00 12.00 15.00 0.00 0.00 0.00 15.00-0.00 0.00 0.00 Mean [m] -0.000004-0.000006 0.000147 Sigma [m] 2.312352 2.110527 0.838594 RMS Error [m] 2.312352 2.110527 0.838594 Min Error and Max Error represent geolocation error intervals between -1.5 and 1.5 times the maximum accuracy of all the images. Columns X, Y, Z show the percentage of images with geolocation errors within the predefined error intervals. The geolocation error is the difference between the intial and computed image positions. Note that the image geolocation errors do not correspond to the accuracy of the observed 3D points. Relative Geolocation Variance Relative Geolocation Error Images X [%] Images Y [%] Images Z [%] [-1.00, 1.00] 96.43 100.00 100.00 [-2.00, 2.00] 100.00 100.00 100.00 [-3.00, 3.00] 100.00 100.00 100.00 Mean of Geolocation Accuracy [m] 5.000000 5.000000 10.000000 Sigma of Geolocation Accuracy [m] 0.000000 0.000000 0.000000 Images X, Y, Z represent the percentage of images with a relative geolocation error in X, Y, Z.

Processing Options Hardware Operating System Camera Model Name Image Coordinate System Output Coordinate System Keypoints Image Scale Advanced: Matching Image Pairs Advanced: Matching Strategy Advanced: Keypoint Extraction Advanced: Calibration CPU: AMD A6-3400M APU with Radeon(tm) HD Graphics RAM: 5GB GPU: AMD Radeon(TM) HD 6520G (Driver: 8.836.1.0), RDPDD Chained DD (Driver: unknown), RDP Encoder Mirror Driver (Driver: unknown), RDP Reflector Display Driver (Driver: unknown) Windows 7 Home Premium, 64-bit CanonIXUS220HS_4.3_4000x3000 (RGB) WGS84 WGS84 / UTM zone 31N (egm96) Full, Image Scale: 1 Aerial Grid or Corridor Use Geometrically Verified Matching: no Targeted Number of Keypoints: Automatic Calibration Method: Standard, Internal Parameters Optimization: All, External Parameters Optimization: All, Rematch: yes Point Cloud Densification details Processing Options Image Scale multiscale, 1/2 (Half image size, Default) Point Density Optimal Minimum Number of Matches 3 3D Textured Mesh Generation yes, Maximum Number of Triangles: 1000000, Texture Size: 8192x8192 Advanced: Matching Window Size 7x7 pixels Advanced: Image Groups group1 Advanced: Use Densification Area yes Advanced: Use Annotations yes Advanced: Limit Camera Depth Automatically no Time for Point Cloud Densification 37m:34s Time for 3D Textured Mesh Generation 01h:52m:32s Results Number of Generated Tiles 1 Number of 3D Densified Points 2808130 Average Density (per m 3 ) 44.37 DSM, Orthomosaic and Index Details Processing Options DSM and Orthomosaic Resolution DSM Filters DSM Generation Time for DSM Generation Time for Orthomosaic Generation 1 x GSD (3.95 [cm/pixel]) Noise Filtering: yes, Surface Smoothing: yes, Sharp yes, Method: Inverse Distance Weighting, Merge Tiles: yes 22m:08s 58m:11s