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Published November 21, 2025 | Version v1
Dataset Open

Data for LookUp3D: Data-Driven 3D Scanning

Description

This deposit contains captured and processed data for LookUp3D, a novel calibration and reconstruction procedure for structured light scanning that foregoes explicit point triangulation in favor of a data-driven lookup procedure. The key idea is to sweep a calibration checkerboard over the entire scanning volume with a linear stage and acquire a dense stack of images to build a per-pixel lookup table from colors to depths. Imperfections in the setup, lens distortion, and sensor defects are baked into the calibration data, leading to a more reliable and accurate reconstruction.

Captured data is provided in TIFF or JPEG file formats, along with the calibrated lookup table files in numpy-specific file formats and a JSON configuration file. Processed data is provided as point clouds (ASCII .ply files) corresponding to figures presented in the paper (plus additional examples) and OBJ (.obj) files used to measure error.

Files

README.md

Files (41.1 GB)

Name Size Download all
data.zip md5:a5a6f023d080be1ff0858e969ea56b72
39.3 GB Preview Download
point_clouds.zip md5:833a735d209fc2f691cf999b0f0bc565
1.7 GB Preview Download
README.md md5:2b10832e77bbe6c6d0f775a6435a0866
4.1 kB Preview Download

Additional details

Related works

Is supplement to
Preprint: arXiv:2405.14882 (arXiv)
Requires
Software: https://www.github.com/geometryprocessing/scanner (URL)

Funding

Collaborative Research: Differentiable Dynamic Simulation on Complex Geometries for Parameter Inference, Design Optimization and Control OAC-2411349
National Science Foundation
Collaborative Research: Frameworks: Cyberinfrastructure to Catalyze and Sustain the Urban Computing Community OAC-2411221
National Science Foundation

Dates

Created
2025