OmniObject3D is a large vocabulary 3D object dataset with massive high-quality real-scanned 3D objects. OmniObject3D has several appealing properties:
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SceneNet is a dataset of labelled synthetic indoor scenes. There are several labeled indoor scenes, including:
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We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quantitatively assess and compare the performance of various inverse rendering methods. Existing real-world datasets typically only consist of the shape and multi-view images of objects, which are not sufficient for evaluating the quality of material recovery and object relighting. Methods capable of recovering material and lighting often resort to synthetic data for quantitative evaluation, which on the other hand does not guarantee generalization to complex real-world environments. We introduce a new dataset of real-world objects captured under a variety of natural scenes with ground-truth 3D scans, multi-view images, and environment l
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OMMO is a new benchmark for several outdoor NeRF-based tasks, such as novel view synthesis, surface reconstruction, and multi-modal NeRF. It contains complex objects and scenes with calibrated images, point clouds and prompt annotations.
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We introduced this dataset in Points2Surf, a method that turns point clouds into meshes.
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It comprises synthetic mesh sequences from Deformation Transfer for Triangle Meshes.
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50K synthetic renders of the human foot, with surface normals, masks and keypoints.
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From https://github.com/MMintLab/VIRDO/blob/master/data/dataset_readme.txt,