The 7-Scenes dataset is a collection of tracked RGB-D camera frames. The dataset may be used for evaluation of methods for different applications such as dense tracking and mapping and relocalization techniques. All scenes were recorded from a handheld Kinect RGB-D camera at 640×480 resolution. The dataset creators use an implementation of the KinectFusion system to obtain the ‘ground truth’ camera tracks, and a dense 3D model. Several sequences were recorded per scene by different users, and split into distinct training and testing sequence sets.

Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the MSR 7Scenes dataset, and the Stanford 12Scenes dataset, as well as precomputed camera coordinate files for both aforementioned datasets. For more information, also see the code documentation: https://github.com/vislearn/dsacstar.

This data is derived from the 7Scenes dataset. It contains graphs used for training PoserNet and for evaluating its performance.

Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the Cambridge Landmarks dataset. We also provide pre-trained models of our method for the MSR 7Scenes dataset and the Cambridge Landmarks dataset. For more information, also see the code documentation: https://github.com/vislearn/LessMore

Dataset containing RGB-D data of 4 large scenes, comprising a total of 12 rooms, for the purpose of RGB and RGB-D camera relocalization. The RGB-D data was captured using a Structure.io depth sensor coupled with an iPad color camera. Each room was scanned multiple times, with the multiple sequences run through a global bundle adjustment in order to obtain globally aligned camera poses though all sequences of the same scene.

Supplementary training data for visual camera re-localization, particularly pre-computed scene coordinates to the MSR 7Scenes dataset and the Standford 12Scenes dataset. We also provide pre-trained models of our method for the 7Scenes, 12Scenes, Dubrovnik and Aachen (day) datasets. For more information, also see the code documentation: https://github.com/vislearn/esac

Newest available Landsat 7 scenes for download (user registration required)

Pre-trained models of our camera re-localization method for the MSR 7Scenes dataset. For more information, also see the code documentation: https://github.com/cvlab-dresden/DSAC

The Indoor-6 dataset was created from multiple sessions captured in six indoor scenes over multiple days. The pseudo ground truth (pGT) 3D point clouds and camera poses for each scene are computed using COLMAP. All training data uses only colmap reconstruction from training images. Compared to 7-scenes, the scenes in Indoor-6 are larger, have multiple rooms, contains illumination variations as the images span multiple days and different times of day.