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A Real World Dataset for Multi-view 3D Reconstruction

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We present a dataset of 998 3D models of everyday tabletop objects along with their 847,000 real world RGB and depth images. Accurate annotation of camera pose and object pose for each image is performed in a semi-automated fashion to facilitate the use of the dataset in a myriad 3D applications like shape reconstruction, object pose estimation, shape retrieval etc. We primarily focus on learned multi-view 3D reconstruction due to the lack of appropriate real world benchmark for the task and demonstrate that our dataset can fill that gap. The entire annotated dataset along with the source code for the annotation tools and evaluation baselines is available at http://www.ocrtoc.org/3d-reconstruction.html.

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Correspondence to Rakesh Shrestha .

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Shrestha, R., Hu, S., Gou, M., Liu, Z., Tan, P. (2022). A Real World Dataset for Multi-view 3D Reconstruction. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_4

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