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| 1 DFKI | +2 Aimmo Germany GmbH | +3 University of Kaiserslautern-Landau | +|||
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| + Blender projects + 3D meshes + | ++ Spherical images + | ++ Depth maps + | ++ Ground truth keypoint correspondences + | +
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+ This paper introduces SphereCraft, a dataset specifically designed for spherical keypoint detection, matching, and camera pose estimation.
+ The dataset addresses the limitations of existing datasets by providing extracted keypoints from various detectors, along with their ground-truth correspondences.
+ Synthetic scenes with photo-realistic rendering and accurate 3D meshes are included, as well as real-world scenes acquired from different spherical cameras.
+ SphereCraft enables the development and evaluation of algorithms targeting multiple camera viewpoints, advancing the state-of-the-art in computer vision tasks involving spherical images.
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+ Our dataset comprises 21 synthetic scenes of different types, sizes, and complexity and yields over 2M image pairs for training and testing spherical keypoint matching models.
+ We generate indoor and outdoor synthetic scenes with high-resolution RGB spherical images along with their depth maps and ground truth camera poses.
+ A selection of popular handcrafted and learned keypoints is then extracted from each image and accurate ground truth keypoint correspondences are established.
+ A highly accurate 3D mesh from each synthetic scene is also included.
+ The resulting data (RGB images, depth maps, camera poses, 3D meshes, keypoints and their correspondences) allows future approaches to be trained and evaluated on exactly the same data.
+ Additionally, we release all Blender projects so other researchers can optionally render the same scenes at different resolutions or create their own version of the data according to their needs.
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+ + For each synthetic scene, we manually place a set of cameras to homogeneously cover it. + We refer to this initial set as anchor cameras. + For each anchor camera, we randomly generate a set of satellite cameras in its vicinity. + Akin to data augmentation, the idea is to automatically produce several novel views of the scene from many different positions and orientations. + For instance, the figure below shows an anchor image (top left) along with its 9 satellite images. + |
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+ The resolution of the rendered spherical images and depth maps is 2048x1024 pixels.
+ Below is a sample from each synthetic scene.
+ The number in parenthesis indicates the number of images in that scene.
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| + Bank (930) + | ++ Barbershop (80) + | ++ Berlin (280) + | +
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| + Classroom (370) + | ++ Garage (1090) + | ++ Harmony (380) + | +
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| + Italian Flat (270) + | ++ Kartu (640) + | ++ Lone Monk (670) + | +
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| + Medieval Port (2160) + | ++ Middle East (4300) + | ++ Passion (600) + | +
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| + Rainbow (930) + | ++ Seoul (330) + | ++ Shapespark (860) + | +
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| + Showroom (1340) + | ++ Simple (310) + | ++ Tokyo (90) + | +
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| + Urban Canyon (4090) + | ++ Vitoria (550) + | ++ Warehouse (900) + | +
| + Real-World Scenes + | +
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+ Along with synthetic scenes, we provide another 9 real scenes, captured with Civetta and Ricoh Theta-S cameras.
+ They convey 4 indoor and 5 outdoor scenes of different sizes and complexity, with resolutions considerably higher than the synthetic images.
+ Images captured with Civetta are 7070x3535 pixels, whereas those acquired with Theta-S are 5376x2688 pixels.
+ Unlike synthetic scenes, here ground truth depth maps, camera poses and keypoint correspondences are not available, but we provide keypoints extracted at the resolutions aforementioned.
+ Once again, the number in parenthesis indicates the number of images in that scene.
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| + Berlin Street (186) + | ++ Church (54) + | ++ Corridors (116) + | +
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| + Meeting Room 1 (18) + | ++ Meeting Room 2 (21) + | ++ Stadium (74) + | +
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| + Town Square (35) + | ++ Train Station (112) + | ++ Uni (71) + | +
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+ Donwload link will be available soon.
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+ Link to SphereCraft's github page will be available soon.
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+ The template for this website is borrowed from Richard Zhang.
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