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
2023 Poster
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.
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