DenseMatch: a dataset for real-time 3D reconstruction

We provide a database aimed at real-time quantitative analysis of 3D reconstruction and alignment methods, containing 3140 point clouds from 10 scenes. These scenes are acquired with a high-resolution 3D scanner. It contains depth maps that produce point clouds with more than 500k points on average. This dataset is useful to develop new models and alignment strategies to automatically reconstruct 3D scenes from data acquired with optical scanners or benchmarking purposes with respect to state-of-the-art methods. The dataset contains two type of files: - \".npz\" files are python readable files containing the point clouds and additional metadata to use. - \".tar.gz\" is a single large zip file which contains the different folders labeled similarly to the pointclouds. Each folder contains the RGB-D images and the camera calibration parameters that were used to reconstruct the 3D data. These are also the original output produced by the 3D scanner used to generate the data. A Python reader for those files is also provided in the project repository which is linked in the notes.