HUMAN4D is a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and $2$ male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc. ), along with multi-RGBD (mRGBD), volumetric and audio data.
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DurLAR is a high-fidelity 128-channel 3D LiDAR dataset with panoramic ambient (near infrared) and reflectivity imagery for multi-modal autonomous driving applications. Compared to existing autonomous driving task datasets, DurLAR has the following novel features:
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Relative Human (RH) contains multi-person in-the-wild RGB images with rich human annotations, including:
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A Simulated Benchmark for multi-modal SLAM Systems Evaluation in Large-scale Dynamic Environments.
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Pano3D is a new benchmark for depth estimation from spherical panoramas. Its goal is to drive progress for this task in a consistent and holistic manner. The Pano3D 360 depth estimation benchmark provides a standard Matterport3D train and test split, as well as a secondary GibsonV2 partioning for testing and training as well. The latter is used for zero-shot cross dataset transfer performance assessment and decomposes it into 3 different splits, each one focusing on a specific generalization axis.
DRACO20K dataset is used for evaluating object canonicalization on methods that estimate a canonical frame from a monocular input image.
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EUEN17037 Daylight and View Standard Test Dataset.
75k photos of windows + 21k synthetic renders of building windows.