We introduce our new dataset, Spaces, to provide a more challenging shared dataset for future view synthesis research. Spaces consists of 100 indoor and outdoor scenes, captured using a 16-camera rig. For each scene, we captured image sets at 5-10 slightly different rig positions (within ∼10cm of each other). This jittering of the rig position provides a flexible dataset for view synthesis, as we can mix views from different rig positions for the same scene during training. We calibrated the intrinsics and the relative pose of the rig cameras using a standard structure from motion approach, using the nominal rig layout as a prior. We corrected exposure differences . For our main experiments we undistort the images and downsample them to a resolution of 800 × 480. We use 90 scenes from the dataset for training and hold out 10 for evaluation.
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The shiny folder contains 8 scenes with challenging view-dependent effects used in our paper. We also provide additional scenes in the shiny_extended folder. The test images for each scene used in our paper consist of one of every eight images in alphabetical order.
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We present a large-scale dataset for 3D urban scene understanding. Compared to existing datasets, our dataset consists of 75 outdoor urban scenes with diverse backgrounds, encompassing over 15,000 images. These scenes offer 360◦ hemispherical views, capturing diverse foreground objects illuminated under various lighting conditions. Additionally, our dataset encompasses scenes that are not limited to forward-driving views, addressing the limitations of previous datasets such as limited overlap and coverage between camera views. The closest pre-existing dataset for generalizable evaluation is DTU [2] (80 scenes) which comprises mostly indoor objects and does not provide multiple foreground objects or background scenes.
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