|Abstract:||Images of a real scene taken with a camera commonly differ from synthetic images of a virtual replica of the same scene, despite advances in light transport simulation and calibration. By explicitly co-developing the scanning hardware and rendering pipeline we are able to achieve negligible per-pixel difference between the real image taken by the camera and the synthesized image on geometrically complex calibration object with known material properties. This approach provides an ideal test-bed for developing data-driven algorithms in the area of 3D reconstruction, as the synthetic data is indistinguishable from real data and can be generated at large scale. Pixel-wise matching also provides an effective way to quantitatively evaluate data-driven reconstruction algorithms.|
|Description:||Benchmark data derived from the synthetic scans to run the denoising, surface reconstruction and shape completion benchmarks. Next to the benchmark data, there is also the collected data used for the different experiments from the paper.|
|Rights:||CC BY 4.0 License|
|Appears in Collections:||Hardware Design and Accurate Simulation for Benchmarking of 3D Reconstruction Algorithms|
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