| This folder contains the reconstructions used in the reconstruction benchmark in the paper. | |
| For each sequence, we provide: | |
| - The input data | |
| - **Images**: hardware synchronised camera images from the three Alphasense cameras | |
| - **Vilens_slam**: undistorted lidar point cloud obtained from VILENS-SLAM. The timestamps are synchronised with the images with motion undistortion. | |
| - **T_gt_lidar.txt**: the global transform between the lidar map and the ground truth map in). This allows one to compare the reconstruction with the ground truth in a single coordinate system. | |
| -Note 1: the raw point cloud is 10 Hz and raw camera is 20 Hz. Here, the point cloud provided comes from the pose-graph SLAM where a node is spawned every 1 metre travelled. The resultant frequency of the camera image and lidar cloud provided is about 1 Hz. | |
| - The reconstructions | |
| - **lidar_cloud_merged_error.pcd**: merged lidar cloud file. | |
| - **nerfacto_cloud_metric_gt_frame_error.pcd**: exported point cloud from nerfacto. | |
| - **openmvs_dense_cloud_gt_frame_error.pcd**: dense MVS point cloud from OpenMVS. | |
| - Note 1: all reconstruction are coloured by point-to-point distance to the ground truth, i.e. reconstruction errors. | |
| - Note 2: all reconstructions are filtered by the ground truth’s occupancy map **gt_cloud.bt** to avoid penalising points in the unknown space. This is described in [SiLVR](https://arxiv.org/abs/2502.02657v1) section V.C.2 | |