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ImageNet3D ImageNet3D: Towards General-Purpose Object-Level 3D Understanding NeurIPS 2024 Wufei Ma 1 Guofeng Zhang 1 Qihao Liu 1 Guanning Zeng 2 Adam Kortylewski 3,4 Yaoyao Liu 1 Alan Yuille 1 1 Johns Hopkins University 2 Tsinghua University 3 University of Freiburg 4 Max Planck Institute for Informatics arXiv Data Code Contents ImageNet3D Tasks Baseline Results Discussion We present ImageNet3D , a large dataset for general-purpose object-level 3D understanding. ImageNet3D augments 200 categories from the ImageNet dataset with 2D bounding box, 3D pose, 3D location annotations, and image captions interleaved with 3D information. With the new annotations available in ImageNet3D, we could (i) analyze the object-level 3D awareness of visual foundation models, (ii) study and develop general-purpose models that infer both 2D and 3D information for arbitrary rigid objects in natural images, and (iii) ...
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