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. <br> 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. <br> <br> The dataset contains two type of files:<br> - ".npz" files are python readable files containing the point clouds and additional metadata to use.<br> - ".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. <br> A Python reader for those files is also provided in the project repository which is linked in the notes.