GGG-BenchmarkSfM- Secondary: Secondary Dataset for Benchmarking Close-range SfM Software Performance Captured with Different Cameras

The proposed dataset is an addition to the one published here - http://dx.doi.org/10.17632/bzxk2n78s9.1 . It is also aimed at benchmarking the performance of SfM software under varying conditions - image positions, but also used cameras. The dataset is comprised on images of 11 objects with varying shapes, sizes and surface textures. It is divided into three parts: - Objects imaged in a semi-circle only from one side - 9 images per object - Objects imaged in a full circle from only one height - 18 images per object - Objects images in a full circle from multiple heights - 54 images per object Three cameras are used to capture the images - Canon 600D, Canon 6D and Canon 5Ds. All images contain EXIF data with used camera parameters. The images do not come with ground truth. If you require image data, plus ground truth meshes for comparison, please refer to this dataset - http://dx.doi.org/10.17632/bzxk2n78s9.

Structure-from-Motion Reconstruction and Reference Microscopy of Severely Eroded Blade

The dataset contains 3 sets of digital elevation models (DEM) from the surface of the leading edge of a wind turbine blade section. The DEMs were acquired from 35 mm × 35 mm regions of interest (RoI) on the blade surface in two different ways; confocal microscopy of a replica of the surface using replica moulding and a rasterized RoI from a high-resolution Structure-from-Motion (SfM) reconstruction. The DEMs are provided as ASCII files with 13 lines of header describing the image size in both pixels and absolute length and width. The confocal microscopy images were acquired using a PLU NEOX confocal microscope by Sensofar with a x5 magnification objective (NA = 0.15). For each image, a 4 × 4 binning was used resulting in a final pixel size of 13.3 μm. The images for the SfM reconstruction were acquired using a Canon 5DS DSLR camera with a variable zoom lens (Canon 70-300 f/4-5.6L IS USM)with the focal length fixed at 300 mm. The reconstruction was performed using Agisoft Metashape and the resultant 3D mesh is available in .obj format. The DEM was created using the software CloudCompare by rasterization and point interpolation of the extracted RoI of the point cloud. The resulting pixel size was chosen to be 13.3 μm to match the reference microscopy images

GGG - Rough or Noisy? Metrics for Noise Detection in SfM Reconstructions

The proposed dataset is part of the paper with the same name "Rough or Noisy? Metrics for Noise Detection in SfM Reconstructions", which proposes 9 geometrical and SfM capturing setup metrics for training a classification method for detecting noise on a SfM reconstructed surfaces. The dataset contains images for Structure from Motion reconstruction of 15 objects. The images were taken using a Canon 5Ds DSLR camera with a resolution of 8688x5792. The images contain EXIF data that can be used to assist the 3D reconstruction. For all objects, except the wind turbine blade the images are 36 in a circle around the object. For the wind turbine blade the images are positioned in 2 vertical bands of 17 images each in a semi-circle. Together with the images there are reconstruction point clouds. These are created using Agisoft Metashape (Photoscan). For easier processing they are separated into: - one file containing vertex positions in X,Y,Z column stacked format, together with color for each vertex position in RGB - one file containing the normals in Nx, Ny, Nz column stacked format - one file containing triangulated faces for the used vertices Finally there are manually annotated ground truth segmentations of the point clouds into noise and not noise for each of the 15 objects

GGG-PositioningSfMScale: Dataset for Testing Scaling and Uncertainty Propagation Through Camera Positioning Data

The proposed dataset can be used to test the precision of scaling SfM reconstructed objects using camera positioning data. In addition position uncertainty data from a DJI GPS RTK in X,Y and Z is added, so the propagation of uncertainty from the positioning sensor to the calculated scale can be calculated. The camera positions are also given with any uncertainty, so other types of position uncertainty can be used to test how the system will react with different uncertainty inputs. Images from two objects are given - an angel statue and a wind turbine blade. Images are taken in a semi-circle pattern around the object and a total of 19 images are taken from each object. The positions of the images are given in a separate file, while all the images contain EXIF data with the used camera parameters. All images are captured using Canon 5Ds DSLR camera. More information on how the scale and scale uncertainty can be calculated are present in the referenced paper

Wind Turbine Blade SfM Image Capturing Setups

The dataset contains 18 image datasets of a wind turbine blade section in an outdoor environment that are used for SfM reconstruction. The images are separated depending on the image capturing setup. The different setups are as follows: - Distance from camera to blade - 2m, 4m, 6m - Horizontal image overlap - number of images taken in a semi-circle around the wind turbine blade in vertical direction - 9, 17, 33 images - Vertical image angle change - number of bands of images, where the height is changed - each band contains an additional semi-circle of horizontal images - 1 or 2 vertical bands Images are taken with a Canon 6D DSLR camera and each image contains EXIF data about the camera settings used to capture it. From the 18 image datasets, 15 were reconstructed correctly using Agisoft Metashape and the resultant 3D meshes are added in .obj format. In addition, a horizontal 2D slice is taken from each 3D reconstruction for testing the captured blade's overall shape

GGG - Rough or Noisy? Metrics for Noise Detection in SfM Reconstructions

The proposed dataset is part of the paper with the same name "Rough or Noisy? Metrics for Noise Detection in SfM Reconstructions", which proposes 9 geometrical and SfM capturing setup metrics for training a classification method for detecting noise on a SfM reconstructed surfaces. The dataset contains images for Structure from Motion reconstruction of 15 objects. The images were taken using a Canon 5Ds DSLR camera with a resolution of 8688x5792. The images contain EXIF data that can be used to assist the 3D reconstruction. For all objects, except the wind turbine blade the images are 36 in a circle around the object. For the wind turbine blade the images are positioned in 2 vertical bands of 17 images each in a semi-circle. Together with the images there are reconstruction point clouds. These are created using Agisoft Metashape (Photoscan). For easier processing they are separated into: - one file containing vertex positions in X,Y,Z column stacked format, together with color for each vertex position in RGB - one file containing the normals in Nx, Ny, Nz column stacked format - one file containing triangulated faces for the used vertices Finally there are manually annotated ground truth segmentations of the point clouds into noise and not noise for each of the 15 objects

GGG-BenchmarkSfM: Dataset for Benchmarking Close-range SfM Software Performance under Varying Capturing Condition

The proposed dataset aims to benchmark the performance of SfM software under varying conditions - different environments, different lighting, image positions, camera setups, etc. Images of six objects are provided with varying shapes, sizes, surface textures and materials. The dataset is divided in two main parts, together with ReadMe files: - Objects and environments data - images from each of the objects both from indoor and outdoor environments are provided. - Capturing setups data - images from one of the objects are provided captured with different setups. Both with and without using a turntable, using one and multiple light sources and different amount of images All images are captured using Canon 6D DSLR camera. All images contain EXIF data with used camera parameters. A ground truth high resolution scanned of each of the objects is provided for verifying the accuracy of the SfM reconstructions

AAU RainSnow Traffic Surveillance Dataset

Rain, Snow, and Bad Weather in Traffic Surveillance Computed vision-based image analysis lays the foundation for automatic traffic surveillance. This works well in daylight when the road users are clearly visible to the camera but often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze, and fog. In this dataset, we have focused on collecting traffic surveillance video in rainfall and snowfall, capturing 22 five-minute videos from seven different traffic intersections. The illumination of the scenes vary from broad daylight to twilight and night. The scenes feature glare from headlights of cars, reflections from puddles, and blur from raindrops at the camera lens. We have collected the data using a conventional RGB colour camera and a thermal infrared camera. If combined, these modalities should enable robust detection and classification of road users even under challenging weather conditions. 100 frames have been selected randomly from each five-minute sequence and any road user in these frames is annotated on a per-pixel, instance-level with corresponding category label. In total, 2,200 frames are annotated, containing 13,297 objects.Rain, Snow, and Bad Weather in Traffic Surveillance Computed vision-based image analysis lays the foundation for automatic traffic surveillance. This works well in daylight when the road users are clearly visible to the camera but often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze, and fog. In this dataset, we have focused on collecting traffic surveillance video in rainfall and snowfall, capturing 22 five-minute videos from seven different traffic intersections. The illumination of the scenes vary from broad daylight to twilight and night. The scenes feature glare from headlights of cars, reflections from puddles, and blur from raindrops at the camera lens. We have collected the data using a conventional RGB colour camera and a thermal infrared camera. If combined, these modalities should enable robust detection and classification of road users even under challenging weather conditions. 100 frames have been selected randomly from each five-minute sequence and any road user in these frames is annotated on a per-pixel, instance-level with corresponding category label. In total, 2,200 frames are annotated, containing 13,297 objects

AAU VAP Trimodal People Segmentation Dataset

Context How do you design a computer vision algorithm that is able to detect and segment people when they are captured by a visible light camera, a thermal infrared camera, and a depth sensor? And how do you fuse the three inherently different data streams such that you can reliably transfer features from one modality to another? Feel free to download our dataset and try it out yourselves! Content The dataset features a total of 5724 annotated frames divided in three indoor scenes. Activity in scene 1 and 3 is using the full depth range of the Kinect for XBOX 360 sensor whereas activity in scene 2 is constrained to a depth range of plus/minus 0.250 m in order to suppress the parallax between the two physical sensors. Scene 1 and 2 are situated in a closed meeting room with little natural light to disturb the depth sensing, whereas scene 3 is situated in an area with wide windows and a substantial amount of sunlight. For each scene, a total of three persons are interacting, reading, walking, sitting, reading, etc. Every person is annotated with a unique ID in the scene on a pixel-level in the RGB modality. For the thermal and depth modalities, annotations are transferred from the RGB images using a registration algorithm found in registrator.cpp. We have used our AAU VAP Multimodal Pixel Annotator to create the ground-truth, pixel-based masks for all three modalities

Sewer-ML

Sewer-ML is a sewer defect dataset. It contains 1.3 million images, from 75,618 videos collected from three Danish water utility companies over nine years. All videos have been annotated by licensed sewer inspectors following the Danish sewer inspection standard, Fotomanualen. This leads to consistent and reliable annotations, and a total of 17 annotated defect classes