Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction

Oleg Voynov, Gleb Bobrovskikh, Pavel Karpyshev, Saveliy Galochkin, Andrei-Timotei Ardelean, Arseniy Bozhenko, Ekaterina Karmanova, Pavel Kopanev, Yaroslav Labutin-Rymsho, Ruslan Rakhimov, Aleksandr Safin, Valerii Serpiva, Alexey Artemov, Evgeny Burnaev, Dzmitry Tsetserukou, Denis Zorin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 21392-21403

Abstract

We present a new multi-sensor dataset for multi-view 3D surface reconstruction. It includes registered RGB and depth data from sensors of different resolutions and modalities: smartphones, Intel RealSense, Microsoft Kinect, industrial cameras, and structured-light scanner. The scenes are selected to emphasize a diverse set of material properties challenging for existing algorithms. We provide around 1.4 million images of 107 different scenes acquired from 100 viewing directions under 14 lighting conditions. We expect our dataset will be useful for evaluation and training of 3D reconstruction algorithms and for related tasks. The dataset is available at skoltech3d.appliedai.tech.

Related Material

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[bibtex]
@InProceedings{Voynov_2023_CVPR, author = {Voynov, Oleg and Bobrovskikh, Gleb and Karpyshev, Pavel and Galochkin, Saveliy and Ardelean, Andrei-Timotei and Bozhenko, Arseniy and Karmanova, Ekaterina and Kopanev, Pavel and Labutin-Rymsho, Yaroslav and Rakhimov, Ruslan and Safin, Aleksandr and Serpiva, Valerii and Artemov, Alexey and Burnaev, Evgeny and Tsetserukou, Dzmitry and Zorin, Denis}, title = {Multi-Sensor Large-Scale Dataset for Multi-View 3D Reconstruction}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2023}, pages = {21392-21403} }