Local Light Field Fusion (LLFF) is a practical and robust deep learning solution for capturing and rendering novel views of complex real-world scenes for virtual exploration. The dataset consists of both renderings and real images of natural scenes. The synthetic images are rendered from the SUNCG and UnrealCV where SUNCG contains 45000 simplistic house and room environments with texture-mapped surfaces and low geometric complexity. UnrealCV contains a few large-scale environments modeled and rendered with extreme detail. The real images are 24 scenes captured from a handheld cellphone.
257 PAPERS • 2 BENCHMARKS
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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ACID consists of thousands of aerial drone videos of different coastline and nature scenes on YouTube. Structure-from-motion is used to get camera poses.
15 PAPERS • 2 BENCHMARKS
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.
15 PAPERS • 1 BENCHMARK
RealEstate10K is a large dataset of camera poses corresponding to 10 million frames derived from about 80,000 video clips, gathered from about 10,000 YouTube videos. For each clip, the poses form a trajectory where each pose specifies the camera position and orientation along the trajectory. These poses are derived by running SLAM and bundle adjustment algorithms on a large set of videos.
9 PAPERS • 2 BENCHMARKS
LightStage is a multi-view dataset, which is proposed in NeuralBody. This dataset captures multiple dynamic human videos using a multi-camera system that has 20+ synchronized cameras. The humans perform complex motions, including twirling, Taichi, arm swings, warmup, punching, and kicking. We provide the SMPL-X parameters recovered with EasyMocap, which contain the motions of body, hand, and face.
7 PAPERS • 1 BENCHMARK
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.
3 PAPERS • 1 BENCHMARK